Conference

## Structured Policy Iteration for Linear Quadratic Regulator

International Conference on Machine Learning (ICML), .

Linear quadratic regulator (LQR) is one of the most popular frameworks to tackle continuous Markov decision process tasks. With its fundamental theory and tractable (More...)
@inproceedings{park-icml20,
author={Youngsuk Park and Ryan A. Rossi and Zheng Wen and Gang Wu and Handong Zhao},
title={Structured Policy Iteration for Linear Quadratic Regulator},
booktitle={International Conference on Machine Learning (ICML)},
year={2020},
}


## net.science: A Cyberinfrastructure for Sustained Innovation in Network Science and Engineering

, Richard Alo, Catherine Amelink, Young Yun Baek, Aashish Chudhary, Kristy Collins, Albert Esterline, , , , Ron Kenyon, , , Dustin Machi, , , Yasuo Miyasaki, , , , , , .

Gateways, .

Networks have entered the mainstream lexicon over the last ten years. This coincides with the pervasive use of networks in a host of disciplines of interest (More...)
@inproceedings{CINES,
author={Nesreen Ahmed and Richard Alo and Catherine Amelink and Young Yun Baek and Aashish Chudhary and Kristy Collins and Albert Esterline and Edward Fox and Geoffrey Fox and Aric Hagberg and Ron Kenyon and Chris J. Kuhlman and Jure Leskovec and Dustin Machi and Madhav V. Marathe and Nataragan Meghanathan and Yasuo Miyasaki and Judy Qiu and Naren Ramakrishnan and S. S. Ravi and Ryan Rossi and Roc Sosic and Gregor von Laszewski},
title={net.science: A Cyberinfrastructure for Sustained Innovation in Network Science and Engineering},
booktitle={Gateways},
year={2020},
}

Journal

## On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications

Transactions on Knowledge Discovery from Data (TKDD), Pages 36, .

Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of (More...)
@inproceedings{rossi20tkdd-roles,
author={Ryan A. Rossi and Di Jin and Sungchul Kim and Nesreen K. Ahmed and Danai Koutra and John Boaz Lee},
title={On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications},
booktitle={Transactions on Knowledge Discovery from Data (TKDD)},
year={2020},
pages={36},
}

Journal

## Ensemble Learning for Relational Data

Journal of Machine Learning Research (JMLR), .

In this work, we present a theoretical analysis framework for relational ensemble models. We show that ensembles of collective classifiers can improve predictions for (More...)
@inproceedings{jmlr20,
author={Hoda Eldardiry and Jennifer Neville and Ryan A. Rossi},
title={Ensemble Learning for Relational Data},
booktitle={Journal of Machine Learning Research (JMLR)},
year={2020},
}

Journal

## Heterogeneous Graphlets

, , Aldo Carranza, , , , .

Transactions on Knowledge Discovery from Data (TKDD), Pages 43, .

In this paper, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed (More...)
@inproceedings{rossi-heterogeneous-graphlets-tkdd,
author={Ryan A. Rossi and Nesreen K. Ahmed and Aldo Carranza and David Arbour and Anup Rao and Sungchul Kim and Eunyee Koh},
title={Heterogeneous Graphlets},
booktitle={Transactions on Knowledge Discovery from Data (TKDD)},
year={2020},
pages={43},
}


## From Community to Role-based Graph Embeddings

arXiv:1908.08572, .

Roles are sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities are sets of nodes (More...)
@inproceedings{from-comm-to-structural-role-embeddings,
author={Ryan A. Rossi and Di Jin and Sungchul Kim and Nesreen K. Ahmed and Danai Koutra and John Boaz Lee},
title={From Community to Role-based Graph Embeddings},
booktitle={arXiv:1908.08572},
year={2019},
}


## Temporal Network Sampling

arXiv:1910.08657, .

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks (More...)
@inproceedings{ahmed19-temporal-network-sampling,
author={Nesreen K. Ahmed and Nick Duffield and Ryan A. Rossi},
title={Temporal Network Sampling},
booktitle={arXiv:1910.08657},
year={2019},
}

Conference

## A Structural Graph Representation Learning Framework

Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), Pages 1-9, .

The success of many graph-based machine learning tasks highly depends on an appropriate representation learned from the graph data. Most work has focused on (More...)
@inproceedings{rossi-wsdm20,
author={Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim and Anup Rao and Yasin Abbasi-Yadkori},
title={A Structural Graph Representation Learning Framework},
booktitle={Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM)},
year={2020},
pages={1-9},
}

Conference

## Real-Time Clustering for Large Sparse Online Visitor Data

Gromit Yeuk-Yin Chan, Fan Du, , , , Cl\'{a}udio T. Silva, Juliana Freire.

Proceedings of The Web Conference (WWW), Pages 1-11, .

Online visitor behaviors are often modeled as a large sparse matrix, where rows represent visitors and columns represent behavior. To discover customer segments with (More...)
@inproceedings{gromit-www20,
author={Gromit Yeuk-Yin Chan and Fan Du and Ryan A. Rossi and Anup Rao and Eunyee Koh and Cl\'{a}udio T. Silva and Juliana Freire},
title={Real-Time Clustering for Large Sparse Online Visitor Data},
booktitle={Proceedings of The Web Conference (WWW)},
year={2020},
pages={1-11},
}

Conference

## Fast and Accurate Estimation of Typed Graphlets

Proceedings of The Web Conference (WWW), .

Typed graphlets are small typed (labeled, colored) induced subgraphs and were recently shown to be the fundamental building blocks of rich complex heterogeneous networks. (More...)
@inproceedings{typed-graphlet-estimation-www20,
author={Ryan A. Rossi and Anup Rao and Tung Mai and Nesreen K. Ahmed},
title={Fast and Accurate Estimation of Typed Graphlets},
booktitle={Proceedings of The Web Conference (WWW)},
year={2020},
}

Conference

## From Closing Triangles to Closing Higher-Order Motifs

Proceedings of The Web Conference (WWW), .

This work introduces higher-order ranking and link prediction methods based on closing higher-order network motifs. In particular, we propose the general notion of a (More...)
@inproceedings{motif-closures-www20,
author={Ryan A. Rossi and Anup Rao and Sungchul Kim and Eunyee Koh and Nesreen K. Ahmed},
title={From Closing Triangles to Closing Higher-Order Motifs},
booktitle={Proceedings of The Web Conference (WWW)},
year={2020},
}

Conference

## Fast Hierarchical Graph Clustering in Linear-Time

Proceedings of The Web Conference (WWW), .

While there has been a lot of research on graph clustering (community detection), most work (i) does not address the hierarchical community detection problem (More...)
@inproceedings{hierarchical-clustering-www20,
author={Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim},
title={Fast Hierarchical Graph Clustering in Linear-Time},
booktitle={Proceedings of The Web Conference (WWW)},
year={2020},
}

Conference

## Approximate Maximum Matching in Random Streams

Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), Pages 1-20, .

In this paper, we study the problem of finding a maximum matching in the semi-streaming model when edges arrive in random order. In the (More...)
@inproceedings{farhadi2020stream-matching,
author={Alireza Farhadi and MohammadTaghi Hajiaghayi and Tung Mai and Anup Rao and Ryan A. Rossi},
title={Approximate Maximum Matching in Random Streams},
booktitle={Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)},
year={2020},
pages={1-20},
}


## Inferring Individual Level Causal Models from Graph-based Relational Time Series

AAAI StarAI, .

In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or (More...)
@inproceedings{causality-aaai20,
author={Ryan A. Rossi and Somdeb Sarkhel and Nesreen K. Ahmed},
title={Inferring Individual Level Causal Models from Graph-based Relational Time Series},
booktitle={AAAI StarAI},
year={2020},
}

Conference

## Graph Convolutional Networks with Motif-based Attention

28th ACM International Conference on Information and Knowledge Management (CIKM), .

The success of deep convolutional neural networks in the domains of computer vision and speech recognition has led researchers to investigate generalizations of the (More...)
@inproceedings{lee19-motif-attention,
author={John Boaz Lee and Ryan Rossi and Xiangnan Kong and Sungchul Kim and Eunyee Koh and Anup Rao},
title={Graph Convolutional Networks with Motif-based Attention},
booktitle={28th ACM International Conference on Information and Knowledge Management (CIKM)},
year={2019},
}

Conference

## On Densification for Minwise Hashing

, , Matt Kapilevich, , , .

UAI, .

One Permutation Hashing (OPH) is a significantly more efficient alternative to the popular minwise hashing. To produce a sketch of size
@inproceedings{mai-uai19,
author={Tung Mai and Anup Rao and Matt Kapilevich and Ryan A. Rossi and Yasin Abbasi-Yadkori and Ritwik Sinha},
title={On Densification for Minwise Hashing},
booktitle={UAI},
year={2019},
}

Journal

## Temporal Network Representation Learning

, Giang Nguyen, , , , .

arXiv, .

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a (More...)
@inproceedings{ctdne-journal,
author={John Boaz Lee and Giang Nguyen and Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim},
title={Temporal Network Representation Learning},
booktitle={arXiv},
year={2019},
}

Conference

## Latent Network Summarization: Bridging Network Embedding and Summarization

KDD, .

An important reason behind the prevalence of node representation learning is their superiority in downstream machine learning tasks on graphs. However, storing the vector-based (More...)
@inproceedings{latent-network-summ-kdd19,
author={Di Jin and Ryan A. Rossi and Eunyee Koh and Sungchul Kim and Anup Rao and Danai Koutra},
title={Latent Network Summarization: Bridging Network Embedding and Summarization},
booktitle={KDD},
year={2019},
}


## Figure Captioning with Reasoning and Sequence-Level Training

Charles Chen, Ruiyi Zhang, , , Scott Cohen, .

IEEE Winter Conference on Applications of Computer Vision (WACV), .

Figures, such as line plots, pie charts, bar charts, are widely used to convey important information in a concise format. In this work, we (More...)
@inproceedings{chen20-fig-caption-generation,
author={Charles Chen and Ruiyi Zhang and Eunyee Koh and Sungchul Kim and Scott Cohen and Ryan A. Rossi},
title={Figure Captioning with Reasoning and Sequence-Level Training},
booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2020},
}

Workshop/symposia

## Heterogeneous Graphlets

, , Aldo Carranza, , , , .

MLG KDD, Pages 8, .

In this work, we generalize the notion of network motifs (graphlets) to heterogeneous networks by introducing the notion of a small induced typed subgraph (More...)
@inproceedings{rossi-heterogeneous-graphlets,
author={Ryan A. Rossi and Nesreen K. Ahmed and Aldo Carranza and David Arbour and Anup Rao and Sungchul Kim and Eunyee Koh},
title={Heterogeneous Graphlets},
booktitle={MLG KDD},
year={2019},
pages={8},
}

Conference

## Node2BITS: Compact Time- and Attribute-aware Node Representations for User Stitching

ECML/PKDD, Pages 22, .

Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world (More...)
@inproceedings{node2bits-ECML19,
author={Di Jin and Mark Heimann and Ryan A. Rossi and Danai Koutra},
title={Node2BITS: Compact Time- and Attribute-aware Node Representations for User Stitching},
booktitle={ECML/PKDD},
year={2019},
pages={22},
}

Journal

## Deep Inductive Graph Representation Learning

IEEE Transactions on Knowledge and Data Engineering (TKDE), Pages 14, .

This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across-networks. In particular, (More...)
@inproceedings{rossi-TKDE18,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed},
title={Deep Inductive Graph Representation Learning},
booktitle={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
year={2018},
pages={14},
}


## Linear Quadratic Regulator for Resource-Efficient Cloud Services

Proceedings of the ACM Symposium on Cloud Computing (SoCC), Pages 488–489, .

@inproceedings{socc19-lqr-cloud,
author={Youngsuk Park and Kanak Mahadik and Ryan A. Rossi and Nesreen K. Ahmed and Gang Wu and Handong Zhao},
title={Linear Quadratic Regulator for Resource-Efficient Cloud Services},
booktitle={Proceedings of the ACM Symposium on Cloud Computing (SoCC)},
year={2019},
pages={488–489},
}


## Linear-time Hierarchical Community Detection

arXiv:1906.06432, .

Community detection in graphs has many important and fundamental applications including in distributed systems, compression, image segmentation, divide-and-conquer graph algorithms such as nested dissection, (More...)
@inproceedings{rossi19-hLP,
author={Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim},
title={Linear-time Hierarchical Community Detection},
booktitle={arXiv:1906.06432},
year={2019},
}


## Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs

arXiv:1906.05059, .

In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing (More...)
@inproceedings{rossi19-motif-closures,
author={Ryan A. Rossi and Anup Rao and Sungchul Kim and Eunyee Koh and Nesreen K. Ahmed and Gang Wu},
title={Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs},
booktitle={arXiv:1906.05059},
year={2019},
}


## Bridging Network Embedding and Graph Summarization

arXiv:1811.04461, .

An important reason behind the prevalence of node representation learning is their superiority in downstream machine learning tasks on graphs. However, storing the vector-based (More...)
@inproceedings{jin18-latent-network-summ,
author={Di Jin and Ryan A. Rossi and Danai Koutra and Eunyee Koh and Sungchul Kim and Anup Rao},
title={Bridging Network Embedding and Graph Summarization},
booktitle={arXiv:1811.04461},
year={2018},
}

Journal

## Attention Models in Graphs: A Survey

Transactions on Knowledge Discovery from Data (TKDD), Pages 19, .

Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as well as their (More...)
@inproceedings{lee18-attention-survey,
author={John Boaz Lee and Ryan A. Rossi and Sungchul Kim and Nesreen K. Ahmed and Eunyee Koh},
title={Attention Models in Graphs: A Survey},
booktitle={Transactions on Knowledge Discovery from Data (TKDD)},
year={2019},
pages={19},
}

Conference

## Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior

WSDM, Pages 9, .

Understanding user behavior and predicting future behavior on the web is critical for providing seamless user experiences as well as increasing revenue of service (More...)
@inproceedings{kim-wsdm19,
author={Donghyun Kim and Sungchul Kim and Handong Zhao and Sheng Li and Ryan A. Rossi and Eunyee Koh},
title={Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior},
booktitle={WSDM},
year={2019},
pages={9},
}


## Higher-order Spectral Clustering for Heterogeneous Graphs

arXiv:1810.02959, Pages 15, .

Higher-order connectivity patterns such as small induced sub-graphs called graphlets (network motifs) are vital to understand the important components (modules/functional units) governing the configuration (More...)
@inproceedings{higher-order-clustering-heter,
author={Aldo G. Carranza and Ryan A. Rossi and Anup Rao and Eunyee Koh},
title={Higher-order Spectral Clustering for Heterogeneous Graphs},
booktitle={arXiv:1810.02959},
year={2018},
pages={15},
}


## Heterogeneous Network Motifs

, , Aldo Carranza, , , , .

arXiv:1901.10026, Pages 18, .

Many real-world applications give rise to large heterogeneous networks where nodes and edges can be of any arbitrary type (e.g., user, web page, location). (More...)
@inproceedings{rossi-heterogeneous-motifs,
author={Ryan A. Rossi and Nesreen K. Ahmed and Aldo Carranza and David Arbour and Anup Rao and Sungchul Kim and Eunyee Koh},
title={Heterogeneous Network Motifs},
booktitle={arXiv:1901.10026},
year={2019},
pages={18},
}


## Neural caption generation over figures

Charles Chen, Ruiyi Zhang, , , Scott Cohen, Tong Yu, , .

Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC), Pages 482-485, .

Figures are human-friendly but difficult for computers to process automatically. In this work, we investigate the problem of figure captioning. The goal is to (More...)
@inproceedings{chen19-neural-fig-caption-generation,
author={Charles Chen and Ruiyi Zhang and Sungchul Kim and Eunyee Koh and Scott Cohen and Tong Yu and Ryan A. Rossi and Razvan Bunescu},
title={Neural caption generation over figures},
booktitle={Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC)},
year={2019},
pages={482-485},
}

Conference

## Graph Classification using Structural Attention

KDD, Pages 1-9, .

Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph (More...)
@inproceedings{lee18-kdd-graph-attention,
author={John Boaz Lee and Ryan A. Rossi and Xiangnan Kong},
title={Graph Classification using Structural Attention},
booktitle={KDD},
year={2018},
pages={1-9},
}


## Higher-order Graph Convolutional Networks

arXiv:1809.07697, Pages 1-8, .

Following the success of deep convolutional networks in various vision and speech related tasks, researchers have started investigating generalizations of the well-known technique for (More...)
@inproceedings{lee18-higher-order-GCNs,
author={John Boaz Lee and Ryan A. Rossi and Xiangnan Kong and Sungchul Kim and Eunyee Koh and Anup Rao},
title={Higher-order Graph Convolutional Networks},
booktitle={arXiv:1809.07697},
year={2018},
pages={1-8},
}

Conference

## Dynamic Network Embeddings: From Random Walks to Temporal Random Walks

Giang Hoang Nguyen, , , , , .

IEEE BigData, Pages 1085-1092, .

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, (More...)
@inproceedings{ctdne-bigdata18,
author={Giang Hoang Nguyen and John Boaz Lee and Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim},
title={Dynamic Network Embeddings: From Random Walks to Temporal Random Walks},
booktitle={IEEE BigData},
year={2018},
pages={1085-1092},
}

Conference

## Interactive Higher-order Network Analysis

IEEE International Conference on Data Mining (ICDM), Pages 6, .

Higher-order network modeling and analysis are vital to understanding the structures governing the configuration and behavior of complex networks. While network motifs are known (More...)
@inproceedings{rossi18icdm,
author={Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh},
title={Interactive Higher-order Network Analysis},
booktitle={IEEE International Conference on Data Mining (ICDM)},
year={2018},
pages={6},
}

Conference

## Relational Similarity Machines (RSM): A Similarity-based Learning Framework for Graphs

IEEE BigData, Pages 10, .

Relational machine learning has become increasingly important due to the recent proliferation and ubiquity of network data. However, existing methods are not designed for (More...)
@inproceedings{rossi18-bigdata,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed and Hoda Eldardiry},
title={Relational Similarity Machines (RSM): A Similarity-based Learning Framework for Graphs},
booktitle={IEEE BigData},
year={2018},
pages={10},
}

Conference

## Predictive Analysis by Leveraging Temporal User Behavior and User Embeddings

Charles Chen, , , , , , .

CIKM, Pages 1-10, .

The rapid growth of mobile devices has resulted in the generation of a large number of user behavior logs that contain latent intentions and (More...)
@inproceedings{chen-cikm18,
author={Charles Chen and Sungchul Kim and Hung Bui and Ryan A. Rossi and Branislav Kveton and Eunyee Koh and Razvan Bunescu},
title={Predictive Analysis by Leveraging Temporal User Behavior and User Embeddings},
booktitle={CIKM},
year={2018},
pages={1-10},
}

Conference

## Higher-Order Network Representation Learning

Proceedings of the 27th International Conference Companion on World Wide Web (WWW), .

This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly (More...)
@inproceedings{rossi-WWW18,
author={Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh},
title={Higher-Order Network Representation Learning},
booktitle={Proceedings of the 27th International Conference Companion on World Wide Web (WWW)},
year={2018},
}

Workshop/symposia

## Deep Inductive Network Representation Learning

Proceedings of the 3rd International Workshop on Learning Representations for Big Networks (WWW BigNet), Pages 8, .

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL (More...)
@inproceedings{rossi-WWW18-BigNet,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed},
title={Deep Inductive Network Representation Learning},
booktitle={Proceedings of the 3rd International Workshop on Learning Representations for Big Networks (WWW BigNet)},
year={2018},
pages={8},
}

Workshop/symposia

## role2vec: Role-based Network Embeddings

DLG KDD, .

Random walks are at the heart of many existing network embedding methods. However, such methods have many limitations that arise from the use of (More...)
@inproceedings{role2vec,
author={Nesreen K. Ahmed and Ryan A. Rossi and John Boaz Lee and Theodore L. Willke and Rong Zhou and Xiangnan Kong and Hoda Eldardiry},
title={role2vec: Role-based Network Embeddings},
booktitle={DLG KDD},
year={2019},
}

Journal

## Estimation of Graphlet Counts in Massive Networks

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Pages 44-57, .

Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been (More...)
@inproceedings{rossi18tnnls,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed},
title={Estimation of Graphlet Counts in Massive Networks},
booktitle={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},
year={2018},
pages={44-57},
}

Workshop/symposia

## Continuous-Time Dynamic Network Embeddings

Giang Hoang Nguyen, , , , , .

Proceedings of the 3rd International Workshop on Learning Representations for Big Networks (WWW BigNet), .

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, (More...)
@inproceedings{nguyen-WWW18,
author={Giang Hoang Nguyen and John Boaz Lee and Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim},
title={Continuous-Time Dynamic Network Embeddings},
booktitle={Proceedings of the 3rd International Workshop on Learning Representations for Big Networks (WWW BigNet)},
year={2018},
}

Workshop/symposia

## Learning Role-based Graph Embeddings

StarAI IJCAI, .

Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of (More...)
@inproceedings{role2vec-ijcai18,
author={Nesreen K. Ahmed and Ryan A. Rossi and Rong Zhou and John Boaz Lee and Xiangnan Kong and Theodore L. Willke and Hoda Eldardiry},
title={Learning Role-based Graph Embeddings},
booktitle={StarAI IJCAI},
year={2018},
}


## HONE: Higher-Order Network Embeddings

arXiv:1801.09303, .

This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly (More...)
@inproceedings{rossi-HONE-arxiv,
author={Ryan A. Rossi and Nesreen K. Ahmed and Eunyee Koh and Sungchul Kim and Anup Rao and Yasin Abbasi-Yadkori},
title={HONE: Higher-Order Network Embeddings},
booktitle={arXiv:1801.09303},
year={2018},
}

Journal

## GraphZIP: A Clique-based Sparse Graph Compression Method

Journal of Big Data, Volume 5, Pages 14, .

Massive graphs are ubiquitous and at the heart of many real-world applications ranging from the World Wide Web to social networks. As a result, (More...)
@article{rossi2018compressing-graphs-cliques,
author={Ryan A. Rossi and Rong Zhou},
title={GraphZIP: A Clique-based Sparse Graph Compression Method},
journal={Journal of Big Data},
volume={5},
number={1},
year={2018},
pages={14},
}

Journal

## Relational Time Series Forecasting

Knowledge Engineering Review (KER), Volume 33, Pages e1, .

Networks encode dependencies between entities (people, computers, proteins) and allow us to study phenomena across social, technological, and biological domains. These networks naturally evolve (More...)
@article{rossi2018ker,
author={Ryan A. Rossi},
title={Relational Time Series Forecasting},
journal={Knowledge Engineering Review (KER)},
volume={33},
year={2018},
pages={e1},
publisher={Cambridge University Press},
}

Journal

## Interactive Visual Graph Mining and Learning

ACM Transactions on Intelligent Systems and Technology, Pages 1-30, .

This paper presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph (More...)
@article{rossi2017graphvis,
author={Ryan A. Rossi and Nesreen K. Ahmed and Hoda Eldardiry and Rong Zhou},
title={Interactive Visual Graph Mining and Learning},
journal={ACM Transactions on Intelligent Systems and Technology},
year={2018},
pages={1-30},
}

Workshop/symposia

## Inductive Representation Learning in Large Attributed Graphs

WiML NIPS, .

Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, (More...)
@inproceedings{ahmed17learning-attr-graphs,
author={Nesreen K. Ahmed and Ryan A. Rossi and Rong Zhou and John Boaz Lee and Xiangnan Kong and Theodore L. Willke and Hoda Eldardiry},
title={Inductive Representation Learning in Large Attributed Graphs},
booktitle={WiML NIPS},
year={2017},
}


## Deep Graph Attention Model

arXiv:1709.06075, Pages 1-8, .

Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when (More...)
@inproceedings{lee17-Deep-Graph-Attention,
author={John Boaz Lee and Ryan Rossi and Xiangnan Kong},
title={Deep Graph Attention Model},
booktitle={arXiv:1709.06075},
year={2017},
pages={1-8},
}


## A Framework for Generalizing Graph-based Representation Learning Methods

arXiv:1709.04596, Pages 1-8, .

Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from (More...)
@inproceedings{ahmed17Gen-Deep-Graph-Learning,
author={Nesreen K. Ahmed and Ryan A. Rossi and Rong Zhou and John Boaz Lee and Xiangnan Kong and Theodore L. Willke and Hoda Eldardiry},
title={A Framework for Generalizing Graph-based Representation Learning Methods},
booktitle={arXiv:1709.04596},
year={2017},
pages={1-8},
}


## Generalizing Deep Learning in Graphs using Attributed Random Walks

arXiv:1709.04596, Pages 1-8, .

Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from (More...)
@inproceedings{ahmed17attrRandomWalks,
author={Nesreen K. Ahmed and Ryan A. Rossi and Rong Zhou and John Boaz Lee and Xiangnan Kong and Theodore L. Willke and Hoda Eldardiry},
title={Generalizing Deep Learning in Graphs using Attributed Random Walks},
booktitle={arXiv:1709.04596},
year={2017},
pages={1-8},
}


## Network Classification and Categorization

James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, , , Karl R. B. Schmitt, .

International Conference on Complex Networks (CompleNet), .

To the best of our knowledge, this paper presents the first large-scale (More...)
@inproceedings{network-classification,
author={James P. Canning and Emma E. Ingram and Sammantha Nowak-Wolff and Adriana M. Ortiz and Nesreen K. Ahmed and Ryan A. Rossi and Karl R. B. Schmitt and Sucheta Soundarajan},
title={Network Classification and Categorization},
booktitle={International Conference on Complex Networks (CompleNet)},
year={2018},
}

Conference

## On Sampling from Massive Graph Streams

VLDB, Pages 1430-1441, .

We propose Graph Priority Sampling (GPS), a new paradigm for order-based reservoir sampling from massive streams of graph edges. GPS provides a general way (More...)
@inproceedings{ahmed17streams,
author={Nesreen K. Ahmed and Nick Duffield and Theodore L. Willke and Ryan A. Rossi},
title={On Sampling from Massive Graph Streams},
booktitle={VLDB},
year={2017},
pages={1430-1441},
}


## Deep Feature Learning for Graphs

arXiv:1704.08829, Pages 1-11, .

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL (More...)
@inproceedings{rossi-deepGL-arxiv,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed},
title={Deep Feature Learning for Graphs},
booktitle={arXiv:1704.08829},
year={2017},
pages={1-11},
}

Conference

## Similarity-based Multi-label Learning

International Joint Conference on Neural Networks (IJCNN), Pages 1-8, .

Multi-label classification is an important learning problem with many applications. In this work, we propose a similarity-based approach for multi-label learning called SML. We (More...)
@inproceedings{rossi18-sml,
author={Ryan A. Rossi and Nesreen K. Ahmed and Hoda Eldardiry and Rong Zhou},
title={Similarity-based Multi-label Learning},
booktitle={International Joint Conference on Neural Networks (IJCNN)},
year={2018},
pages={1-8},
}


## Estimation of Graphlet Statistics

arXiv:1701.01772, Pages 1-14, .

Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been (More...)
@inproceedings{rossi17graphlet-est,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed},
title={Estimation of Graphlet Statistics},
booktitle={arXiv:1701.01772},
year={2017},
pages={1-14},
}

Conference

## Edge Role Discovery via Higher-order Structures

PAKDD, Pages 1-12, .

Previous work in network analysis has focused on modeling the roles of nodes in graphs. In this paper, we introduce edge role discovery and (More...)
@inproceedings{ahmed2017roles,
author={Nesreen K. Ahmed and Ryan A. Rossi and Theodore L. Willke and Rong Zhou},
title={Edge Role Discovery via Higher-order Structures},
booktitle={PAKDD},
year={2017},
pages={1-12},
publisher={Springer},
}


## Revisiting Role Discovery in Networks: From Node to Edge Roles

arXiv preprint arXiv:1610.00844, .

Previous work in network analysis has focused on modeling the mixed-memberships of node roles in the graph, but not the roles of edges. We introduce the (More...)
@article{ahmed2016revisiting,
author={Nesreen K. Ahmed and Ryan A. Rossi and Theodore L. Willke and Rong Zhou},
title={Revisiting Role Discovery in Networks: From Node to Edge Roles},
journal={arXiv preprint arXiv:1610.00844},
year={2016},
}

Workshop/symposia

## A Higher-order Latent Space Network Model

Proceedings of the AAAI PAIR (Plan, Activity, and Intent Recognition) Workshop, Pages 1-7, .

Previous work in network analysis has focused on model- ing node roles in the graph. In this work, we introduce edge role discovery and (More...)
@inproceedings{ahmed17aaai,
author={Nesreen K. Ahmed and Ryan A. Rossi and Theodore L. Willke and Rong Zhou},
title={A Higher-order Latent Space Network Model},
booktitle={Proceedings of the AAAI PAIR (Plan, Activity, and Intent Recognition) Workshop},
year={2017},
pages={1-7},
}

Conference

## Estimation of Local Subgraph Counts

Proceedings of the IEEE International Conference on BigData, Pages 586-595, .

Graphlets represent small induced subgraphs and are becoming increasingly important for a variety of applications. Despite the importance of the local subgraph (graphlet) counting (More...)
@inproceedings{ahmed16bigdata,
author={Nesreen K. Ahmed and Theodore L. Willke and Ryan A. Rossi},
title={Estimation of Local Subgraph Counts},
booktitle={Proceedings of the IEEE International Conference on BigData},
year={2016},
pages={586-595},
}

Journal

## Graphlet Decomposition: Framework, Algorithms, and Applications

Knowledge and Information Systems (KAIS), Pages 689-722, .

From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks. While graphlets have witnessed (More...)
@article{ahmed2016kais,
author={Nesreen K. Ahmed and Jennifer Neville and Ryan A. Rossi and Nick Duffield and Theodore L. Willke},
title={Graphlet Decomposition: Framework, Algorithms, and Applications},
journal={Knowledge and Information Systems (KAIS)},
year={2016},
pages={689-722},
}

Workshop/symposia

## Relational Similarity Machines

Proceedings of the 12th International Workshop on Mining and Learning with Graphs (MLG), Pages 1-8, .

This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance (More...)
@inproceedings{rossi16rsm,
author={Ryan A. Rossi and Rong Zhou and Nesreen K. Ahmed},
title={Relational Similarity Machines},
booktitle={Proceedings of the 12th International Workshop on Mining and Learning with Graphs (MLG)},
year={2016},
pages={1-8},
}

Conference

## Leveraging Multiple GPUs and CPUs for Graphlet Counting in Large Networks

ACM International Conference on Information and Knowledge Management (CIKM), Pages 1783-1792, .

Massively parallel architectures such as the GPU are becoming increasingly important due to the recent proliferation of data. In this paper, we propose a (More...)
@inproceedings{rossi16cikm,
author={Ryan A. Rossi and Rong Zhou},
title={Leveraging Multiple GPUs and CPUs for Graphlet Counting in Large Networks},
booktitle={ACM International Conference on Information and Knowledge Management (CIKM)},
year={2016},
pages={1783-1792},
}


## Exact and Estimation of Local Edge-centric Graphlet Counts

KDD BigMine, Pages 16, .

Graphlets represent small induced subgraphs and are becoming increasingly important for a variety of applications. Despite the importance of the local graphlet problem, existing (More...)
@inproceedings{ahmed16bigmine,
author={Nesreen Ahmed and Ted Willke and Ryan A. Rossi},
title={Exact and Estimation of Local Edge-centric Graphlet Counts},
booktitle={KDD BigMine},
year={2016},
pages={16},
}

Journal

## Parallel Collective Factorization for Modeling Large Heterogeneous Networks

Social Network Analysis and Mining (SNAM), Pages 30, .

Relational learning methods for heterogeneous network data are becoming increasingly important for many real-world applications. However, existing relational learning approaches are (More...)
@inproceedings{rossi16factorization,
author={Ryan A. Rossi and Rong Zhou},
title={Parallel Collective Factorization for Modeling Large Heterogeneous Networks},
booktitle={Social Network Analysis and Mining (SNAM)},
year={2016},
pages={30},
}

Conference

## Efficient Graphlet Counting for Large Networks

ICDM, Pages 1-10, .

From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as (More...)
@inproceedings{ahmed2015icdm,
author={Nesreen K. Ahmed and Jennifer Neville and Ryan A. Rossi and Nick Duffield},
title={Efficient Graphlet Counting for Large Networks},
booktitle={ICDM},
year={2015},
pages={1-10},
}

Conference

## Toward Interactive Relational Learning

Proceedings of the AAAI Conference on Artificial Intelligence, Pages 4383-4384, .

This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users interactively design relational models by specifying the various components, constraints, and (More...)
@inproceedings{rossi2016aaai,
author={Ryan Rossi and Rong Zhou},
title={Toward Interactive Relational Learning},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2016},
pages={4383-4384},
}

Journal

## An Interactive Data Repository with Visual Analytics

SIGKDD Explor., Volume 17, Pages 37-41, .

Scientific data repositories have historically made data widely accessible to the scientific community, and have led to better research through comparisons, reproducibility, as well (More...)
@article{nr-sigkdd16,
author={Ryan A. Rossi and Nesreen K. Ahmed},
title={An Interactive Data Repository with Visual Analytics},
journal={SIGKDD Explor.},
volume={17},
number={2},
year={2016},
pages={37-41},
publisher={ACM},
}

Conference

## Scalable Relational Learning for Large Heterogeneous Networks

IEEE International Conference on Data Science and Advanced Analytics (DSAA), Pages 1-10, .

Relational models for heterogeneous network data are becoming increasingly important for many real-world applications. However, existing relational learning approaches are not parallel, have scalability (More...)
@inproceedings{rossi2015dsaa-pcmf,
author={Ryan A. Rossi and Rong Zhou},
title={Scalable Relational Learning for Large Heterogeneous Networks},
booktitle={IEEE International Conference on Data Science and Advanced Analytics (DSAA)},
year={2015},
pages={1-10},
}

Conference

## Interactive Visual Graph Analytics on the Web

International AAAI Conference on Web and Social Media (ICWSM), Pages 566-569, .

We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for (More...)
@inproceedings{ahmed-icwsm15,
author={Nesreen K. Ahmed and Ryan A. Rossi},
title={Interactive Visual Graph Analytics on the Web},
booktitle={International AAAI Conference on Web and Social Media (ICWSM)},
year={2015},
pages={566-569},
}

Conference

## The Network Data Repository with Interactive Graph Analytics and Visualization

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), .

Network Repository (NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML (More...)
@inproceedings{nr-aaai15,
author={Ryan A. Rossi and Nesreen K. Ahmed},
title={The Network Data Repository with Interactive Graph Analytics and Visualization},
booktitle={Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI)},
year={2015},
}

Journal

## Parallel Maximum Clique Algorithms with Applications to Network Analysis

SIAM Journal on Scientific Computing (SISC), Volume 37, Pages 28, .

We present a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The (More...)
@article{rossi2015pmc-sisc,
author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin},
title={Parallel Maximum Clique Algorithms with Applications to Network Analysis},
journal={SIAM Journal on Scientific Computing (SISC)},
volume={37},
number={5},
year={2015},
pages={28},
publisher={Society for Industrial and Applied Mathematics (SIAM)},
}

Journal

## Role Discovery in Networks

IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 27, Pages 1112-1131, .

Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, (More...)
@article{rossi2015roles,
author={Ryan A. Rossi and Nesreen K. Ahmed},
title={Role Discovery in Networks},
journal={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
volume={27},
number={4},
year={2015},
pages={1112-1131},
publisher={IEEE},
}

Journal

## Coloring Large Complex Networks

Social Network Analysis and Mining, Volume 4, Pages 37, .

Given a large social or information network, how can we partition the vertices into sets (i.e., colors) such that no two vertices linked by an edge (More...)
@article{rossi2014coloring,
author={Ryan A. Rossi and Nesreen K. Ahmed},
title={Coloring Large Complex Networks},
journal={Social Network Analysis and Mining},
volume={4},
number={1},
year={2014},
pages={37},
}

Conference

## Fast Triangle Core Decomposition for Mining Large Graphs

Advances in Knowledge Discovery and Data Mining (PAKDD), Pages 310-322, .

Large triangle cores represent dense subgraphs for which each edge has at least k − 2 triangles (same as cliques). This paper presents a fast algorithm (More...)
@inproceedings{rossi2014pakdd,
author={Ryan A. Rossi},
title={Fast Triangle Core Decomposition for Mining Large Graphs},
booktitle={Advances in Knowledge Discovery and Data Mining (PAKDD)},
year={2014},
pages={310-322},
publisher={Springer},
}

Conference

## A Multi-Level Approach for Evaluating Internet Topology Generators

Networking, Pages 1-9, .

The topology of a network (connectivity of autonomous systems (ASes) or routers) has significant implications on the design of protocols and applications, and on the placement of (More...)
@inproceedings{rossi2013topology,
author={Ryan A. Rossi and Sonia Fahmy and Nilothpal Talukder},
title={A Multi-Level Approach for Evaluating Internet Topology Generators},
booktitle={Networking},
year={2013},
pages={1-9},
}

Conference

## Fast Maximum Clique Algorithms for Large Graphs

, , , Mostofa A. Patwary.

Proceedings of the 23rd International Conference on World Wide Web (WWW), .

We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. Despite clique’s status as (More...)
@inproceedings{rossi2014pmc-www,
author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and Mostofa A. Patwary},
title={Fast Maximum Clique Algorithms for Large Graphs},
booktitle={Proceedings of the 23rd International Conference on World Wide Web (WWW)},
year={2014},
}

Workshop/symposia

## Triangle Core Decomposition and Maximum Cliques

SIAM Workshop on Network Science, Pages 1-2, .

Consider a graph G = (V, E). A k-core of G is a maximal induced subgraph of G where each vertex has degree at least (More...)
@inproceedings{rossi2013trianglecores,
author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin},
title={Triangle Core Decomposition and Maximum Cliques},
booktitle={SIAM Workshop on Network Science},
year={2013},
pages={1-2},
}

Journal

## A Dynamical System for PageRank with Time-Dependent Teleportation

Internet Mathematics, Volume 10, Pages 188-217, .

We propose a dynamical system that captures changes to the network centrality of nodes as external interest in those nodes varies. We derive this system (More...)
@article{rossi2014dynamical,
author={David F. Gleich and Ryan A. Rossi},
title={A Dynamical System for PageRank with Time-Dependent Teleportation},
journal={Internet Mathematics},
volume={10},
number={1-2},
year={2014},
pages={188-217},
}

Conference

## Modeling Dynamic Behavior in Large Evolving Graphs

, , , Keith Henderson.

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM), Pages 667-676, .

Given a large time-evolving graph, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model (More...)
@inproceedings{rossi2013modeling,
author={Ryan A. Rossi and Brian Gallagher and Jennifer Neville and Keith Henderson},
title={Modeling Dynamic Behavior in Large Evolving Graphs},
booktitle={Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM)},
year={2013},
pages={667-676},
publisher={ACM},
}

Journal

## Transforming Graph Data for Statistical Relational Learning

Journal of Artificial Intelligence Research (JAIR), Volume 45, Pages 363-441, .

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase (More...)
@article{rossi2012transforming,
author={Ryan A. Rossi and Luke K. McDowell and David W. Aha and Jennifer Neville},
title={Transforming Graph Data for Statistical Relational Learning},
journal={Journal of Artificial Intelligence Research (JAIR)},
volume={45},
year={2012},
pages={363-441},
publisher={AAAI Press},
}


## Dynamic PageRank using Evolving Teleportation

Algorithms and Models for the Web Graph, Volume 7323, Pages 126-137, .

The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an (More...)
@article{rossi2012dynamic,
author={Ryan A. Rossi and David F. Gleich},
title={Dynamic PageRank using Evolving Teleportation},
journal={Algorithms and Models for the Web Graph},
volume={7323},
series={Lecture Notes in Computer Science},
editor={Anthony Bonato and Jeannette Janssen},
year={2012},
pages={126-137},
publisher={Springer},
}


## Transforming graph representations for statistical relational learning

arXiv:1204.0033, .

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a (More...)
@article{rossi2012relational-rep-learning,
author={Ryan A. Rossi and Luke K. McDowell and David W. Aha and Jennifer Neville},
title={Transforming graph representations for statistical relational learning},
journal={arXiv:1204.0033},
year={2012},
}


## Representations and Ensemble Methods for Dynamic Relational Classification

arXiv:1111.5312, .

Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain (More...)
@article{rossi11representations,
author={Ryan A. Rossi and Jennifer Neville},
title={Representations and Ensemble Methods for Dynamic Relational Classification},
journal={arXiv:1111.5312},
year={2011},
}


## Role-Dynamics: Fast Mining of Large Dynamic Networks

, , , Keith Henderson.

Proceedings of the 21st International Conference Companion on World Wide Web (WWW), Pages 997-1006, .

To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present (More...)
@inproceedings{rossi2012role,
author={Ryan A. Rossi and Brian Gallagher and Jennifer Neville and Keith Henderson},
title={Role-Dynamics: Fast Mining of Large Dynamic Networks},
booktitle={Proceedings of the 21st International Conference Companion on World Wide Web (WWW)},
year={2012},
pages={997-1006},
}

Conference

## Time-evolving Relational Classification and Ensemble Methods

PAKDD, Pages 1-13, .

Relational networks often evolve over time by the addition, deletion, and changing of links, nodes, and attributes. However, accurately incorporating the full range of temporal dependencies (More...)
@inproceedings{rossi2012dynamic-srl,
author={Ryan A. Rossi and Jennifer Neville},
title={Time-evolving Relational Classification and Ensemble Methods},
booktitle={PAKDD},
year={2012},
pages={1-13},
publisher={Springer},
}

Workshop/symposia

## Modeling the Evolution of Discussion Topics and Communication to Improve Relational Classification

SIGKDD SOMA, Pages 89-97, .

Textual analysis is one means by which to assess communication type and moderate the influence of network structure in predictive models of individual behavior. (More...)
@inproceedings{rossi2010modeling,
author={Ryan A. Rossi and Jennifer Neville},
title={Modeling the Evolution of Discussion Topics and Communication to Improve Relational Classification},
booktitle={SIGKDD SOMA},
year={2010},
pages={89-97},
}

Conference

## Cricks Hypothesis Revisited: The Existence of a Universal Coding Frame

, , Axel E. Bernal.

AINAW, Volume 1, Pages 745-751, .

Presented in the US, Russia, Japan, Thailand and Canada at various conferences and keynotes.

In 1957 Crick hypothesized that the genetic code was a comma free code. This property would imply the existence of a universal coding frame (More...)
@inproceedings{rossi2007crick,
author={Jean-Louis Lassez and Ryan A. Rossi and Axel E. Bernal},
title={Cricks Hypothesis Revisited: The Existence of a Universal Coding Frame},
booktitle={AINAW},
volume={1},
year={2007},
pages={745-751},
}

Conference

## Ranking Links on the Web: Search and Surf Engines

, , Kumar Jeev.

New Frontiers in Applied Artificial Intelligence (IEA/AIE), Pages 199-208, .

The main algorithms at the heart of search engines have focused on ranking and classifying sites. This is appropriate when we know what we (More...)
@article{lassez2008ranking,
author={Jean-Louis Lassez and Ryan A. Rossi and Kumar Jeev},
title={Ranking Links on the Web: Search and Surf Engines},
journal={New Frontiers in Applied Artificial Intelligence (IEA/AIE)},
year={2008},
pages={199-208},
publisher={Springer},
}

Conference

## Signature based Intrusion Detection using Latent Semantic Analysis

, , Stephen Sheel, Srinivas Mukkamala.

Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Pages 1068-1074, .

We address the problem of selecting and extracting key features by using singular value decomposition and latent semantic analysis. As a consequence, we are (More...)
@inproceedings{lassez2008signature,
author={Jean-Louis Lassez and Ryan A. Rossi and Stephen Sheel and Srinivas Mukkamala},
title={Signature based Intrusion Detection using Latent Semantic Analysis},
booktitle={Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN)},
year={2008},
pages={1068-1074},
}

Conference

## Client-side Dynamic Metadata in Web 2.0

John Stamey, , Daniel Boorn, .

Proceedings of the 25th annual ACM International Conference on Design of Communication (SIGDOC), Pages 155-161, .

@inproceedings{stamey2007dynamic,
author={John Stamey and Jean-Louis Lassez and Daniel Boorn and Ryan A. Rossi},
title={Client-side Dynamic Metadata in Web 2.0},
booktitle={Proceedings of the 25th annual ACM International Conference on Design of Communication (SIGDOC)},
year={2007},
pages={155-161},
}

Conference

## Latent Semantic Analysis of the Languages of Life

Computational Intelligence and Intelligent Systems, Pages 128-137, .

We use Latent Semantic Analysis as a basis to study the languages of life. Using this approach we derive techniques to discover latent relationships (More...)
@article{rossi2009latent,
author={Ryan A. Rossi},
title={Latent Semantic Analysis of the Languages of Life},
journal={Computational Intelligence and Intelligent Systems},
year={2009},
pages={128-137},
publisher={Springer},
}

Conference

## A Scalable Image Processing Framework for Gigapixel Mars and Other Celestial Body Images

, , Khawaja S. Shams.

IEEE Aerospace, Pages 1-11, .

The Mars Reconnaissance Orbiter's HiRISE (High Resolution Imaging Science Experiment) camera takes the largest images of the Martian surface. The image size is typically (More...)
@inproceedings{powell2010scalable,
author={Mark W. Powell and Ryan A. Rossi and Khawaja S. Shams},
title={A Scalable Image Processing Framework for Gigapixel Mars and Other Celestial Body Images},
booktitle={IEEE Aerospace},
year={2010},
pages={1-11},
}

Conference

## Polyphony: A Workflow Orchestration Framework for Cloud Computing

Khawaja S. Shams, , Tom M. Crockett, Jeffrey S. Norris, , Tom Soderstrom.

10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), Pages 606-611, .

Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (More...)
@inproceedings{shams2010polyphony,
author={Khawaja S. Shams and Mark W. Powell and Tom M. Crockett and Jeffrey S. Norris and Ryan A. Rossi and Tom Soderstrom},
title={Polyphony: A Workflow Orchestration Framework for Cloud Computing},
booktitle={10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid)},
year={2010},
pages={606-611},
}

Conference

## Automatically Identifying Relations in Privacy Policies

John W. Stamey, .

Proceedings of the 27th ACM International Conference on Design of Communication, Pages 233-238, .

@inproceedings{stamey2009automatically,
author={John W. Stamey and Ryan A. Rossi},
title={Automatically Identifying Relations in Privacy Policies},
booktitle={Proceedings of the 27th ACM International Conference on Design of Communication},
year={2009},
pages={233-238},
}

Technical report

## Modeling the Evolution of the Internet Topology: A Multi-Level Evaluation Framework

Tech. Report Purdue CS, Pages 1-10, .

The topology of a network (connectivity of autonomous systems (ASes) or routers) has significant implications on the design of protocols and applications, and on the placement of (More...)
@inproceedings{rossi2013modeling-evol,
author={Ryan A. Rossi and Sonia Fahmy and Nilothpal Talukder},
title={Modeling the Evolution of the Internet Topology: A Multi-Level Evaluation Framework},
booktitle={Tech. Report Purdue CS},
year={2013},
pages={1-10},
}

Technical report

## A Fast Parallel Maximum Clique Algorithm for Large Sparse Graphs and Temporal Strong Components

, , , Mostofa A. Patwary.

arXiv preprint arXiv:1302.6256, Pages 1-9, .

We propose a fast, parallel, maximum clique algorithm for large, sparse graphs that is designed to exploit characteristics of social and information networks. We observe roughly linear (More...)
@article{rossi2013parallel-cliques,
author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and Mostofa A. Patwary},
title={A Fast Parallel Maximum Clique Algorithm for Large Sparse Graphs and Temporal Strong Components},
journal={arXiv preprint arXiv:1302.6256},
year={2013},
pages={1-9},
}

Technical report

## What if CLIQUE were fast? Maximum Cliques in Information Networks and Strong Components in Temporal Networks

, , , Mostofa A. Patwary.

arXiv preprint arXiv:1210.5802, Pages 1-11, .

Exact maximum clique finders have progressed to the point where we can investigate cliques in million-node social and information networks, as well as find strongly connected components (More...)
@article{rossi2012fastclique,
author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and Mostofa A. Patwary},
title={What if CLIQUE were fast? Maximum Cliques in Information Networks and Strong Components in Temporal Networks},
journal={arXiv preprint arXiv:1210.5802},
year={2012},
pages={1-11},
}

Technical report

## Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

, , , Keith Henderson.

DOE Scientific and Technical Information, LLNL-TR-514271, Pages 1-10, .

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model (More...)
@inproceedings{rossi2011modeling,
author={Ryan A. Rossi and Brian Gallagher and Jennifer Neville and Keith Henderson},
title={Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model},
booktitle={DOE Scientific and Technical Information, LLNL-TR-514271},
year={2011},
pages={1-10},
publisher={DOE},
}

Technical report

## Discovering Latent Graphs with Positive and Negative Links to Eliminate Spam

JPL Tech. Report, Pages 1-9, .

This paper proposes a new direction in Adversarial Information Retrieval through automatically ranking links. We use techniques based on Latent Semantic Analysis to define (More...)
@inproceedings{rossi2009discovering,
author={Ryan A. Rossi},
title={Discovering Latent Graphs with Positive and Negative Links to Eliminate Spam},
booktitle={JPL Tech. Report},
year={2009},
pages={1-9},
}


## Improving Relational Machine Learning by Modeling Temporal Dependencies

Purdue University, Pages 163, .

Ph.D. Dissertation, Purdue University

Networks encode dependencies between entities (people, computers, proteins) and allow us to study phenomena across social, technological, and biological domains. These networks naturally evolve (More...)
@phdthesis{rossi2015purdue,
author={Ryan A. Rossi},
title={Improving Relational Machine Learning by Modeling Temporal Dependencies},
school={Purdue University},
year={2015},
pages={163},
publisher={ProQuest},
}

Patent

## One-class Similarity Machines for Anomaly Detection

, Ajay Raghavan, Jungho Park.

Patent, .

Patent application filed

One-class similarity machines is a family of unsupervised novelty/anomaly detection methods. We have used it for detecting anomalies in multi-variate time series data. Performance (More...)
@misc{rossi18oneClassSim,
author={Ryan A. Rossi and Ajay Raghavan and Jungho Park},
title={One-class Similarity Machines for Anomaly Detection},
booktitle={Patent},
year={2017},
yearfiled={2018},
}

Patent

## Higher-Order Network Embedding

Patent, .

Patent application filed, USPTO App. #16/204,616

In implementations of higher-order network embedding, a computing device maintains interconnected data in the form of a graph that represents a network, the graph including nodes (More...)
@misc{rossi18hone-patent,
author={Ryan A. Rossi and Sungchul Kim and Eunyee Koh},
title={Higher-Order Network Embedding},
booktitle={Patent},
year={2018},
yearfiled={2018},
}

Patent

## Latent Network Summarization

Patent, .

Patent application filed, USPTO App. #16/252,169

An important reason behind the prevalence of node representation learning is their superiority in downstream machine learning tasks on graphs. However, storing the vector-based (More...)
@misc{latent-network-summ-patent,
author={Di Jin and Ryan A. Rossi and Eunyee Koh and Sungchul Kim and Anup Rao},
title={Latent Network Summarization},
booktitle={Patent},
year={2018},
yearfiled={2019},
}

Patent

## Time-Dependent Network Embedding

Patent, .

Patent application filed, USPTO App. #16/192,313

In implementations of time-dependent network embedding, a computing device maintains time-dependent interconnected data in the form of a time-based graph that includes nodes and node associations (More...)
@misc{rossi18time-dependent-network-embedding,
author={Ryan A. Rossi and Sungchul Kim and Eunyee Koh},
title={Time-Dependent Network Embedding},
booktitle={Patent},
year={2018},
yearfiled={2018},
}

Patent

## System and Method for Anomaly Characterization Based on Joint Historical and Time-series Analysis

Jungho Park, Ajay Raghavan, , Yosuke Tajika, Akira Minegishi, Tetsuyoshi Ogura.

Patent, .

Patent application filed, USPTO App. #16/170,815

One embodiment provides a system for facilitating anomaly detection and characterization. During operation, the system determines, by a computing device, a first set of testing data (More...)
@misc{park18JointAnomaly,
author={Jungho Park and Ajay Raghavan and Ryan A. Rossi and Yosuke Tajika and Akira Minegishi and Tetsuyoshi Ogura},
title={System and Method for Anomaly Characterization Based on Joint Historical and Time-series Analysis},
booktitle={Patent},
year={2017},
yearfiled={2018},
}

Patent

## Binned IQR for Anomaly Detection in Multivariate Time Series

Ajay Raghavan, , Jungho Park.

Patent, .

Patent application filed

This invention presents a new method to detect anomalies in multivariate time series data using "binned" IQR (Inter-Quartile Range). The IQR method is commonly (More...)
@misc{ajay18binnedIQR,
author={Ajay Raghavan and Ryan A. Rossi and Jungho Park},
title={Binned IQR for Anomaly Detection in Multivariate Time Series},
booktitle={Patent},
year={2017},
yearfiled={2018},
}

Patent

## Method and System for Similarity-based Multi-label Learning

Patent, .

Patent application filed, USPTO App. #16/237,439

A system is provided for facilitating multi-label classification. During operation, the system maintains a set of training vectors. A respective vector represents an (More...)
@misc{rossi17multilabel,
author={Ryan A. Rossi and Hoda Eldardiry},
title={Method and System for Similarity-based Multi-label Learning},
booktitle={Patent},
year={2017},
yearfiled={2017},
}

Patent

## Deep Graph Representation Learning

Patent, .

US Patent No. 10482375

@misc{rossi17deepRML,
author={Ryan A. Rossi and Rong Zhou},
title={Deep Graph Representation Learning},
booktitle={Patent},
year={2019},
yearfiled={2016},
}

Patent

## A Graph Search Engine

Patent, .

Patent pending

@misc{rossi17graphSearchEngine,
author={Ryan A. Rossi and Rong Zhou},
title={A Graph Search Engine},
booktitle={Patent},
year={2017},
yearfiled={2016},
}

Patent

## Deep Relational Learning

Patent, .

Patent application filed

@misc{rossi16deep-relational-learning,
author={Ryan A. Rossi and Rong Zhou},
title={Deep Relational Learning},
booktitle={Patent},
year={2016},
yearfiled={2015},
}

Patent

## Fast and Accurate Unbiased Graphlet Estimation

Patent, .

Patent application filed, USPTO App. #15/179724

@misc{rossi16patent-graphlet-estimation,
author={Ryan A. Rossi and Rong Zhou},
title={Fast and Accurate Unbiased Graphlet Estimation},
booktitle={Patent},
year={2016},
yearfiled={2015},
}

Patent

## Efficient Parallel Hybrid CPU-GPU Graph Mining and Learning via Induced Subgraph Features

Patent, .

Patent application filed

@misc{rossi15patent-hybrid-cpu-gpu-graphlets,
author={Ryan A. Rossi and Rong Zhou},
title={Efficient Parallel Hybrid CPU-GPU Graph Mining and Learning via Induced Subgraph Features},
booktitle={Patent},
year={2016},
yearfiled={2016},
}

Patent

## System And Method For Compressing Graphs Via Cliques

Patent, .

Patent application filed, USPTO App. #15/183561

@misc{rossi15patent-clique-compression,
author={Ryan A. Rossi and Rong Zhou},
title={System And Method For Compressing Graphs Via Cliques},
booktitle={Patent},
year={2016},
yearfiled={2015},
}

Patent

## Localized Visual Graph Filters for Complex Graph Queries

Patent, .

Patent application filed, USPTO App. #15/175751

@misc{rossi16patent-localized-visual,
author={Ryan A. Rossi and Rong Zhou},
title={Localized Visual Graph Filters for Complex Graph Queries},
booktitle={Patent},
year={2016},
yearfiled={2015},
}

Patent

## Computer-implemented System And Method For Relational Time Series Learning

US Patent No. 10438130, .

Patent application filed, USPTO App. #14/955965

@misc{rossi16patent-rel-time-series,
author={Ryan A. Rossi and Rong Zhou},
title={Computer-implemented System And Method For Relational Time Series Learning},
booktitle={US Patent No. 10438130},
year={2016},
yearfiled={2014},
}

Patent

## Parallel Collective Matrix Factorization Framework for Big Data

Patent, .

US Patent No. 10235403

A system and a method perform matrix factorization. According to the system and the method, at least one matrix is received. The at least (More...)
@misc{rossi16patent-pcmf,
author={Ryan A. Rossi and Rong Zhou},
title={Parallel Collective Matrix Factorization Framework for Big Data},
booktitle={Patent},
year={2015},
yearfiled={2014},
}


## Higher-Order Clustering for Heterogeneous Networks via Typed Motifs

arXiv:1810.02959, Pages 15, .

Higher-order connectivity patterns such as small induced sub-graphs called graphlets (network motifs) are vital to understand the important components (modules/functional units) governing the configuration (More...)
@inproceedings{higher-order-clustering-heter-arxiv,
author={Aldo G. Carranza and Ryan A. Rossi and Anup Rao and Eunyee Koh},
title={Higher-Order Clustering for Heterogeneous Networks via Typed Motifs},
booktitle={arXiv:1810.02959},
year={2018},
pages={15},
}

Book

## Introduction to Bioinformatics Using Action Labs

, , Stephen Sheel.

Book ISBN 1329925912, .

Bioinformatics is the application of computational techniques and tools to analyze and manage biological data. This book provides an introduction to bioinformatics through the (More...)
@article{introBINF,
author={Jean-Louis Lassez and Ryan A. Rossi and Stephen Sheel},
title={Introduction to Bioinformatics Using Action Labs},
journal={Book ISBN 1329925912},
year={2009},
}


## Research Experience

Member of Research Staff, Palo Alto Research Center
Visiting Researcher, Palo Alto Research Center (Xerox PARC)
Research Fellow, Purdue University (2009-2012)

Research Assistant, Lawrence Livermore National Laboratory (ISCR)
LLNL Scholar: Cyber Defenders Program (2011-2012)

Research Assistant, Naval Research Laboratory, AI Research Center
Relational Representation Discovery in Statistical Relational Learning, (Summer 2010)

Research Assistant, Coastal Carolina University (2005-2009)
Advisor: Jean-Louis Lassez, Retired IBM T.J. Watson Research Center
(Mathematics Genealogy Project)

Research Assistant, NASA Jet Propulsion Laboratory, (Summer 2009)
California Institute of Technology, Space Grant/USRP Fellowship
(Returned to continue my research).

Research Assistant, NASA Jet Propulsion Laboratory, (Spring 2009)
California Institute of Technology, USRP NASA Fellowship
Advisor: Mark Powell(Scalable Image Processing) and Khawaja Shams(Cloud Computing)

Research Assistant, University of Massachusetts at Amherst, KDL, (Summer 2008)

Research Assistant, New Mexico Tech, Institute for Complex Additive Systems
Advisor: Srinivas Mukkamala, Senior Research Scientist, ICASA (Summer 2007)

## Teaching Experience

Search Engine Theory, Instructor, Spring 2008
This course was taught from a machine learning perspective using a variety of resources and recent papers along with a series of homeworks and projects implementing the significant parts of a search engine.

Algorithms in Bioinformatics, Teaching Assistant, Fall 2007
Numerical Methods, Teaching Assistant, Spring 2007
Introduction to Bioinformatics, Teaching Assistant, Fa 2008, Fa/Spr 2007, Spr 2006
Introduction to Algorithm Design II, Teaching Assistant, Spring 2006
Introduction to Algorithm Design I, Teaching Assistant, Spring 2006

As a teaching assistant I gave lectures and review sessions; developed homeworks, labs, and programs, held office hours, and maintained course website.

## Books / Lecture Notes

Bioinformatics is the application of computational techniques and tools to analyze and manage biological data. This book provides an Introduction to Bioinformatics through the use of Action Labs. These labs allow students to get experience using real data and tools to solve difficult problems. The book comes with supplementary slides, papers, and tools. The labs use data from Breast Cancer, Liver Disease, Diabetes, SARS, HIV, Extinct Organisms, and many others. The book has been written for first or second year computer science, mathematics, and biology students. The book is published by the Digital University Press. [pdf version] (6.2 MB)

## Research Positions

• Present 2015

#### Member of Research Staff

Palo Alto Research Center

• 2015 2009

#### Ph.D. Fellow

Purdue University, Computer Science

• 2015 2013

#### Visiting Researcher

Palo Alto Research Center

## Education

• Ph.D. 2015

Ph.D. in Computer Science

Purdue University

• M.S.2013

Master of Science in Computer Science

Purdue University

• B.S.2009

Bachelor of Science in Computer Science

CCU

## Honors and Awards

• 2015
Purdue Bilsland Dissertation Fellowship
• 2012
DoD NDSEG Fellow
• 2009
National Science Foundation GRFP Award

## Research Positions

• 2017-Present
• 2015-2017
Member of Research Staff, Palo Alto Research Center (Xerox PARC)
• 2009-2015
Research Fellow, Department of Computer Science, Purdue University
• 2013-2015
Visiting Researcher, Palo Alto Research Center (PARC)
• 2011-2012
Research Assistant, Lawrence Livermore National Laboratory (ISCR)
• Summer 2010
Research Assistant, Naval Research Laboratory (Artificial Intelligence Center)
• 2009
Research Assistant, NASA Jet Propulsion Laboratory/California Institute of Technology
• Summer 2008
Research Assistant, University of Massachusetts at Amherst (UMass Amherst)