Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms. - Danai Koutra; Tai-You Ke; U Kang; Duen Horng Chau; Hsing-Kuo Kenneth Pao; Christos Faloutsos (ECML/PKDD (2), 2011)

RolX: structural role extraction & mining in large graphs. - Keith Henderson; Brian Gallagher; Tina Eliassi-Rad; Hanghang Tong; Sugato Basu; Leman Akoglu; Danai Koutra; Christos Faloutsos; Lei Li 005 (KDD, 2012)

TensorSplat: Spotting Latent Anomalies in Time. - Danai Koutra; Evangelos E. Papalexakis; Christos Faloutsos (Panhellenic Conference on Informatics, 2012)

OPAvion: mining and visualization in large graphs. - Leman Akoglu; Duen Horng Chau; U Kang; Danai Koutra; Christos Faloutsos (SIGMOD Conference, 2012)

NetSimile: A Scalable Approach to Size-Independent Network Similarity - Michele Berlingerio; Danai Koutra; Tina Eliassi-Rad; Christos Faloutsos (CoRR, 2012)

Network similarity via multiple social theories. - Michele Berlingerio; Danai Koutra; Tina Eliassi-Rad; Christos Faloutsos (ASONAM, 2013)

BIG-ALIGN: Fast Bipartite Graph Alignment. - Danai Koutra; Hanghang Tong; David M. Lubensky (ICDM, 2013)

Detecting insider threats in a real corporate database of computer usage activity. - Ted E. Senator; Henry G. Goldberg; Alex Memory; William T. Young; Brad Rees; Robert Pierce; Daniel Huang 003; Matthew Reardon; David A. Bader; Edmond Chow; Irfan A. Essa; Joshua Jones; et al. (KDD, 2013)

Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model. - Danai Koutra; Vasileios Koutras; B. Aditya Prakash; Christos Faloutsos (PAKDD (1), 2013)

DELTACON: A Principled Massive-Graph Similarity Function. - Christos Faloutsos; Danai Koutra; Joshua T. Vogelstein (SDM, 2013)

Fast anomaly detection despite the duplicates. - Jay Yoon Lee; U Kang; Danai Koutra; Christos Faloutsos (WWW (Companion Volume), 2013)

Com2: Fast Automatic Discovery of Temporal ('Comet') Communities. - Miguel Araujo; Spiros Papadimitriou; Stephan Günnemann; Christos Faloutsos; Prithwish Basu; Ananthram Swami; Evangelos E. Papalexakis; Danai Koutra (PAKDD (2), 2014)

Net-Ray: Visualizing and Mining Billion-Scale Graphs. - U Kang; Jay Yoon Lee; Danai Koutra; Christos Faloutsos (PAKDD (1), 2014)

Influence Propagation: Patterns, Model and a Case Study. - Yibin Lin; Agha Ali Raza; Jay Yoon Lee; Danai Koutra; Roni Rosenfeld; Christos Faloutsos (PAKDD (1), 2014)

VOG: Summarizing and Understanding Large Graphs. - Danai Koutra; U Kang; Jilles Vreeken; Christos Faloutsos (SDM, 2014)

Glance: rapidly coding behavioral video with the crowd. - Walter S. Lasecki; Mitchell L. Gordon; Danai Koutra; Malte F. Jung; Steven P. Dow; Jeffrey P. Bigham (UIST, 2014)

Linearized and Turbo Belief Propagation. - Wolfgang Gatterbauer; Stephan Günnemann; Danai Koutra; Christos Faloutsos (CoRR, 2014)

If walls could talk: Patterns and anomalies in Facebook wallposts. - Pravallika Devineni; Danai Koutra; Michalis Faloutsos; Christos Faloutsos (ASONAM, 2015)

TimeCrunch: Interpretable Dynamic Graph Summarization. - Neil Shah; Danai Koutra; Tianmin Zou; Brian Gallagher; Christos Faloutsos (KDD, 2015)

Events and Controversies: Influences of a Shocking News Event on Information Seeking. - Danai Koutra; Paul N. Bennett; Eric Horvitz (WWW, 2015)

An Empirical Comparison of the Summarization Power of Graph Clustering Methods. - Yike Liu; Neil Shah; Danai Koutra (CoRR, 2015)

Graph based anomaly detection and description: a survey. - Leman Akoglu; Hanghang Tong; Danai Koutra (Data Min. Knowl. Discov., 2015)

Linearized and Single-Pass Belief Propagation. - Wolfgang Gatterbauer; Stephan Günnemann; Danai Koutra; Christos Faloutsos (Proc. VLDB Endow., 2015)

Perseus: An Interactive Large-Scale Graph Mining and Visualization Tool. - Danai Koutra; Di Jin 003; Yuanshi Ning; Christos Faloutsos (Proc. VLDB Endow., 2015)

Summarizing and understanding large graphs. - Danai Koutra; U Kang; Jilles Vreeken; Christos Faloutsos (Stat. Anal. Data Min., 2015)

Coding Varied Behavior Types Using the Crowd. - Jinyeong Yim; Jeel Jasani; Aubrey Henderson; Danai Koutra; Steven Dow; Winnie Leung; Ellen Lim; Mitchell L. Gordon; Jeffrey P. Bigham; Walter S. Lasecki (CSCW Companion, 2016)

On Skewed Multi-dimensional Distributions: the FusionRP Model, Algorithms, and Discoveries. - Venkata Krishna Pillutla; Zhanpeng Fang; Pravallika Devineni; Christos Faloutsos; Danai Koutra; Jie Tang 001 (SDM, 2016)

A Graph Summarization: A Survey. - Yike Liu; Abhilash Dighe; Tara Safavi; Danai Koutra (CoRR, 2016)

Discovery of "comet" communities in temporal and labeled graphs Com^2. - Miguel Araujo; Stephan Günnemann; Spiros Papadimitriou; Christos Faloutsos; Prithwish Basu; Ananthram Swami; Evangelos E. Papalexakis; Danai Koutra (Knowl. Inf. Syst., 2016)

DeltaCon: Principled Massive-Graph Similarity Function with Attribution. - Danai Koutra; Neil Shah; Joshua T. Vogelstein; Brian Gallagher; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2016)

One Size Does Not Fit All: Profiling Personalized Time-Evolving User Behaviors. - Pravallika Devineni; Evangelos E. Papalexakis; Danai Koutra; A. Seza Dogruöz; Michalis Faloutsos (ASONAM, 2017)

BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks. - Amanda J. Minnich; Nikan Chavoshi; Danai Koutra; Abdullah Mueen (ASONAM, 2017)

Exploratory Analysis of Graph Data by Leveraging Domain Knowledge. - Di Jin 003; Danai Koutra (ICDM, 2017)

Inferring, Summarizing and Mining Multi-source Graph Data. - Danai Koutra (ICDM Workshops, 2017)

Scalable Hashing-Based Network Discovery. - Tara Safavi; Chandra Sekhar Sripada; Danai Koutra (ICDM, 2017)

PNP: Fast Path Ensemble Method for Movie Design. - Danai Koutra; Abhilash Dighe; Smriti Bhagat; Udi Weinsberg; Stratis Ioannidis; Christos Faloutsos; Jean Bolot (KDD, 2017)

Edge Labeling Schemes for Graph Data. - Oshini Goonetilleke; Danai Koutra; Timos Sellis; Kewen Liao (SSDBM, 2017)

Topic-based Social Influence Measurement for Social Networks. - Asso Hamzehei; Shanqing Jiang; Danai Koutra; Raymond K. Wong 001; Fang Chen 001 (Australas. J. Inf. Syst., 2017)

Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit. - Josh Gardner 001; Danai Koutra; Jawad Mroueh; Victor Pang; Arya Farahi; Sam Krassenstein; Jared Webb (CoRR, 2017)

On Summarizing Large-Scale Dynamic Graphs. - Neil Shah; Danai Koutra; Lisa Jin; Tianmin Zou; Brian Gallagher; Christos Faloutsos (IEEE Data Eng. Bull., 2017)

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs. - Di Jin 003; Aristotelis Leventidis; Haoming Shen; Ruowang Zhang; Junyue Wu; Danai Koutra (Informatics, 2017)

Facebook wall posts: a model of user behaviors. - Pravallika Devineni; Danai Koutra; Michalis Faloutsos; Christos Faloutsos (Soc. Netw. Anal. Min., 2017)

Individual and Collective Graph Mining: Principles, Algorithms, and Applications - Danai Koutra; Christos Faloutsos (Individual and Collective Graph Mining, 2017)

t-PNE: Tensor-Based Predictable Node Embeddings. - Saba A. Al-Sayouri; Ekta Gujral; Danai Koutra; Evangelos E. Papalexakis; Sarah S. Lam (ASONAM, 2018)

REGAL: Representation Learning-based Graph Alignment. - Mark Heimann; Haoming Shen; Tara Safavi; Danai Koutra (CIKM, 2018)

GeoAlign: Interpolating Aggregates over Unaligned Partitions. - Jie Song 013; Danai Koutra; Murali Mani; H. V. Jagadish (EDBT, 2018)

Summarizing Graphs at Multiple Scales: New Trends. - Danai Koutra; Jilles Vreeken; Francesco Bonchi (ICDM, 2018)

Career Transitions and Trajectories: A Case Study in Computing. - Tara Safavi; Maryam Davoodi; Danai Koutra (KDD, 2018)

HashAlign: Hash-Based Alignment of Multiple Graphs. - Mark Heimann; Wei Lee; Shengjie Pan; Kuan-Yu Chen; Danai Koutra (PAKDD (3), 2018)

Fast Flow-based Random Walk with Restart in a Multi-query Setting. - Yujun Yan; Mark Heimann; Di Jin 003; Danai Koutra (SDM, 2018)

GeoFlux: Hands-Off Data Integration Leveraging Join Key Knowledge. - Jie Song 013; Danai Koutra; Murali Mani; H. V. Jagadish (SIGMOD Conference, 2018)

Node Representation Learning for Multiple Networks: The Case of Graph Alignment. - Mark Heimann; Haoming Shen; Danai Koutra (CoRR, 2018)

Bridging Network Embedding and Graph Summarization. - Di Jin 003; Ryan A. Rossi; Danai Koutra; Eunyee Koh; Sungchul Kim; Anup Rao 002 (CoRR, 2018)

Graph Summarization Methods and Applications: A Survey. - Yike Liu; Tara Safavi; Abhilash Dighe; Danai Koutra (ACM Comput. Surv., 2018)

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome. - Geoffrey D. Hannigan; Melissa Beth Duhaime; Danai Koutra; Patrick D. Schloss (PLoS Comput. Biol., 2018)

Reducing large graphs to small supergraphs: a unified approach. - Yike Liu; Tara Safavi; Neil Shah; Danai Koutra (Soc. Netw. Anal. Min., 2018)

When to remember where you came from: node representation learning in higher-order networks. - Caleb Belth; Fahad Kamran; Donna Tjandra; Danai Koutra (ASONAM, 2019)

The Effect of Social Interaction on Facilitating Audience Participation in a Live Music Performance. - Sang Won Lee 002; Aaron Willette; Danai Koutra; Walter S. Lasecki (Creativity & Cognition, 2019)

Distribution of Node Embeddings as Multiresolution Features for Graphs. - Mark Heimann; Tara Safavi; Danai Koutra (ICDM, 2019)

Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket. - Tara Safavi; Caleb Belth; Lukas Faber; Davide Mottin; Emmanuel Müller; Danai Koutra (ICDM, 2019)

Smart Roles: Inferring Professional Roles in Email Networks. - Di Jin 003; Mark Heimann; Tara Safavi; Mengdi Wang; Wei Lee; Lindsay Snider; Danai Koutra (KDD, 2019)

Latent Network Summarization: Bridging Network Embedding and Summarization. - Di Jin 003; Ryan A. Rossi; Eunyee Koh; Sungchul Kim; Anup Rao 002; Danai Koutra (KDD, 2019)

GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data. - Yujun Yan; Jiong Zhu; Marlena Duda; Eric Solarz; Chandra Sekhar Sripada; Danai Koutra (KDD, 2019)

node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. - Di Jin 003; Mark Heimann; Ryan A. Rossi; Danai Koutra (ECML/PKDD (1), 2019)

Coupled Clustering of Time-Series and Networks. - Yike Liu; Linhong Zhu; Pedro A. Szekely; Aram Galstyan; Danai Koutra (SDM, 2019)

SURREAL: Subgraph Robust Representation Learning. - Saba A. Al-Sayouri; Danai Koutra; Evangelos E. Papalexakis; Sarah S. Lam (Appl. Netw. Sci., 2019)

From Community to Role-based Graph Embeddings. - Ryan A. Rossi; Di Jin 003; Sungchul Kim; Nesreen K. Ahmed; Danai Koutra; John Boaz Lee (CoRR, 2019)

On effective and efficient graph edge labeling. - Oshini Goonetilleke; Danai Koutra; Kewen Liao; Timos Sellis (Distributed Parallel Databases, 2019)

Fast network discovery on sequence data via time-aware hashing. - Tara Safavi; Chandra Sekhar Sripada; Danai Koutra (Knowl. Inf. Syst., 2019)

Collaborative topic regression for predicting topic-based social influence. - Asso Hamzehei; Raymond K. Wong 001; Danai Koutra; Fang Chen 001 (Mach. Learn., 2019)

Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach. - Pin-Yu Chen; Chun-Chen Tu; Pai-Shun Ting; Ya-Yun Lo; Danai Koutra; Alfred O. Hero III (IEEE Trans. Signal Inf. Process. over Networks, 2019)

CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. - Xiyuan Chen; Mark Heimann; Fatemeh Vahedian; Danai Koutra (CIKM, 2020)

G-CREWE: Graph CompREssion With Embedding for Network Alignment. - Kyle Kai Qin; Flora D. Salim; Yongli Ren; Wei Shao 006; Mark Heimann; Danai Koutra (CIKM, 2020)

Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. - Josh Gardner 001; Jawad Mroueh; Natalia Jenuwine; Noah Weaverdyck; Samuel Krassenstein; Arya Farahi; Danai Koutra (DSAA, 2020)

CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. - Tara Safavi; Danai Koutra (EMNLP (1), 2020)

Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction. - Tara Safavi; Danai Koutra; Edgar Meij (EMNLP (1), 2020)

Message from the workshop chairs. - Scott McMillan; Manoj Kumar 006; Danai Koutra; Mahantesh Halappanavar; Tim Mattson; Antonino Tumeo (IPDPS Workshops, 2020)

Mining Persistent Activity in Continually Evolving Networks. - Caleb Belth; Xinyi Zheng; Danai Koutra (KDD, 2020)

Neural Execution Engines: Learning to Execute Subroutines. - Yujun Yan; Kevin Swersky; Danai Koutra; Parthasarathy Ranganathan; Milad Hashemi (NeurIPS, 2020)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. - Jiong Zhu; Yujun Yan; Lingxiao Zhao; Mark Heimann; Leman Akoglu; Danai Koutra (NeurIPS, 2020)

SpecGreedy: Unified Dense Subgraph Detection. - Wenjie Feng 001; Shenghua Liu; Danai Koutra; Huawei Shen; Xueqi Cheng (ECML/PKDD (1), 2020)

Toward Activity Discovery in the Personal Web. - Tara Safavi; Adam Fourney; Robert Sim; Marcin Juraszek; Shane Williams; Ned Friend; Danai Koutra; Paul N. Bennett (WSDM, 2020)

What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. - Caleb Belth; Xinyi Zheng; Jilles Vreeken; Danai Koutra (WWW, 2020)

Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data. - Josh Gardner 001; Jawad Mroueh; Natalia Jenuwine; Noah Weaverdyck; Samuel Krassenstein; Arya Farahi; Danai Koutra (CoRR, 2020)

Improving the Utility of Knowledge Graph Embeddings with Calibration. - Tara Safavi; Danai Koutra; Edgar Meij (CoRR, 2020)

Consistent Network Alignment with Node Embedding. - Xiyuan Chen; Mark Heimann; Fatemeh Vahedian; Danai Koutra (CoRR, 2020)

Generalizing Graph Neural Networks Beyond Homophily. - Jiong Zhu; Yujun Yan; Lingxiao Zhao; Mark Heimann; Leman Akoglu; Danai Koutra (CoRR, 2020)

From Static to Dynamic Node Embeddings. - Di Jin 003; Sungchul Kim; Ryan A. Rossi; Danai Koutra (CoRR, 2020)

Generating Negative Commonsense Knowledge. - Tara Safavi; Danai Koutra (CoRR, 2020)

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. - Shengpu Tang; Parmida Davarmanesh; Yanmeng Song; Danai Koutra; Michael W. Sjoding; Jenna Wiens (J. Am. Medical Informatics Assoc., 2020)

t-PINE: tensor-based predictable and interpretable node embeddings. - Saba A. Al-Sayouri; Ekta Gujral; Danai Koutra; Evangelos E. Papalexakis; Sarah S. Lam (Soc. Netw. Anal. Min., 2020)

On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications. - Ryan A. Rossi; Di Jin 003; Sungchul Kim; Nesreen K. Ahmed; Danai Koutra; John Boaz Lee (ACM Trans. Knowl. Discov. Data, 2020)

Graph Neural Networks with Heterophily. - Jiong Zhu; Ryan A. Rossi; Anup Rao 002; Tung Mai; Nedim Lipka; Nesreen K. Ahmed; Danai Koutra (AAAI, 2021)

Relational World Knowledge Representation in Contextual Language Models: A Review. - Tara Safavi; Danai Koutra (EMNLP (1), 2021)

NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases. - Tara Safavi; Jing Zhu 005; Danai Koutra (EMNLP (1), 2021)

A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning. - Nishil Talati; Di Jin 003; Haojie Ye; Ajay Brahmakshatriya; Ganesh S. Dasika; Saman P. Amarasinghe; Trevor N. Mudge; Danai Koutra; Ronald G. Dreslinski (IISWC, 2021)

Refining Network Alignment to Improve Matched Neighborhood Consistency. - Mark Heimann; Xiyuan Chen; Fatemeh Vahedian; Danai Koutra (SDM, 2021)

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. - Jing Zhu 005; Xingyu Lu; Mark Heimann; Danai Koutra (SDM, 2021)

Towards Understanding and Evaluating Structural Node Embeddings. - Junchen Jin; Mark Heimann; Di Jin 003; Danai Koutra (CoRR, 2021)

Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. - Jiong Zhu; Junchen Jin; Michael T. Schaub; Danai Koutra (CoRR, 2021)

Convolutional Neural Network Dynamics: A Graph Perspective. - Fatemeh Vahedian; Ruiyu Li; Puja Trivedi; Di Jin 003; Danai Koutra (CoRR, 2021)

Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. - Di Jin 003; Bunyamin Sisman; Hao Wei; Xin Luna Dong; Danai Koutra (Proc. VLDB Endow., 2021)

The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability. - Danai Koutra (Proc. VLDB Endow., 2021)

CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. - Jing Zhu 005; Danai Koutra; Mark Heimann (CIKM, 2022)

Leveraging the Graph Structure of Neural Network Training Dynamics. - Fatemeh Vahedian; Ruiyu Li; Puja Trivedi; Di Jin 003; Danai Koutra (CIKM, 2022)

How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation. - Ekdeep Singh Lubana; Puja Trivedi; Danai Koutra; Robert P. Dick (CoLLAs, 2022)

Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. - Yujun Yan; Milad Hashemi; Kevin Swersky; Yaoqing Yang; Danai Koutra (ICDM, 2022)

How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. - Jiong Zhu; Junchen Jin; Donald Loveland; Michael T. Schaub; Danai Koutra (KDD, 2022)

Analyzing Data-Centric Properties for Graph Contrastive Learning. - Puja Trivedi; Ekdeep Singh Lubana; Mark Heimann; Danai Koutra; Jayaraman J. Thiagarajan (NeurIPS, 2022)

On Generalizing Static Node Embedding to Dynamic Settings. - Di Jin 003; Sungchul Kim; Ryan A. Rossi; Danai Koutra (WSDM, 2022)

Accepted Tutorials at The Web Conference 2022. - Riccardo Tommasini 001; Senjuti Basu Roy; Xuan Wang 008; Hongwei Wang; Heng Ji 001; Jiawei Han 001; Preslav Nakov; Giovanni Da San Martino; Firoj Alam; Markus Schedl; Elisabeth Lex; Akash Bharadwaj; et al. (WWW (Companion Volume), 2022)

Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. - Puja Trivedi; Ekdeep Singh Lubana; Yujun Yan; Yaoqing Yang; Danai Koutra (WWW, 2022)

Learning node embeddings via summary graphs: a brief theoretical analysis. - Houquan Zhou 002; Shenghua Liu; Danai Koutra; Huawei Shen; Xueqi Cheng (CoRR, 2022)

On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. - Donald Loveland; Jiong Zhu; Mark Heimann; Benjamin Fish; Michael T. Schaub; Danai Koutra (CoRR, 2022)

Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (CoRR, 2022)

Analyzing Data-Centric Properties for Contrastive Learning on Graphs. - Puja Trivedi; Ekdeep Singh Lubana; Mark Heimann; Danai Koutra; Jayaraman J. Thiagarajan (CoRR, 2022)

A hidden challenge of link prediction: which pairs to check? - Caleb Belth; Alican Büyükçakir; Danai Koutra (Knowl. Inf. Syst., 2022)

Toward Understanding and Evaluating Structural Node Embeddings. - Junchen Jin; Mark Heimann; Di Jin 003; Danai Koutra (ACM Trans. Knowl. Discov. Data, 2022)

A Provable Framework of Learning Graph Embeddings via Summarization. - Houquan Zhou 002; Shenghua Liu; Danai Koutra; Huawei Shen; Xueqi Cheng (AAAI, 2023)

A Closer Look At Scoring Functions And Generalization Prediction. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (ICASSP, 2023)

A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (ICLR, 2023)

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). - Jiaqi Ma 001; Jiong Zhu; Yuxiao Dong; Danai Koutra; Jingrui He; Qiaozhu Mei; Anton Tsitsulin; Xingjian Zhang 002; Marinka Zitnik (KDD, 2023)

Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. - Gaotang Li; Marlena Duda; Xiang Zhang 012; Danai Koutra; Yujun Yan (KDD, 2023)

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. - Donald Loveland; Jiong Zhu; Mark Heimann; Benjamin Fish; Michael T. Schaub; Danai Koutra (LoG, 2023)

Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective. - Yujun Yan; Gaotang Li; Danai Koutra (CoRR, 2023)

SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks. - Jing Zhu 005; Yuhang Zhou; Vassilis N. Ioannidis; Shengyi Qian 001; Wei Ai 002; Xiang Song 003; Danai Koutra (CoRR, 2023)

Leveraging Graph Diffusion Models for Network Refinement Tasks. - Puja Trivedi; Ryan A. Rossi; David Arbour; Tong Yu 001; Franck Dernoncourt; Sungchul Kim; Nedim Lipka; Namyong Park; Nesreen K. Ahmed; Danai Koutra (CoRR, 2023)

Heterophily and Graph Neural Networks: Past, Present and Future. - Jiong Zhu; Yujun Yan; Mark Heimann; Lingxiao Zhao; Leman Akoglu; Danai Koutra (IEEE Data Eng. Bull., 2023)

Scalable Graph Mining and Learning (Dagstuhl Seminar 23491). - Danai Koutra; Henning Meyerhenke; Ilya Safro; Fabian Brandt-Tumescheit (Dagstuhl Reports, 2023)

Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation. - Yuhang Zhou; Jing Zhu 005; Paiheng Xu; Xiaoyu Liu 003; Xiyao Wang; Danai Koutra; Wei Ai 002; Furong Huang (EMNLP (Findings), 2024)

On Estimating Link Prediction Uncertainty Using Stochastic Centering. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (ICASSP, 2024)

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. - Puja Trivedi; Mark Heimann; Rushil Anirudh; Danai Koutra; Jayaraman J. Thiagarajan (ICLR, 2024)

Editing Partially Observable Networks via Graph Diffusion Models. - Puja Trivedi; Ryan A. Rossi; David Arbour; Tong Yu 001; Franck Dernoncourt; Sungchul Kim; Nedim Lipka; Namyong Park; Nesreen K. Ahmed; Danai Koutra (ICML, 2024)

On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks. - Jiong Zhu; Gaotang Li; Yao-An Yang; Jing Zhu 005; Xuehao Cui; Danai Koutra (NeurIPS, 2024)

TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. - Jing Zhu 005; Xiang Song 003; Vassilis N. Ioannidis; Danai Koutra; Christos Faloutsos (SIGIR, 2024)

Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices. - Jing Zhu 005; Yuhang Zhou; Vassilis N. Ioannidis; Shengyi Qian 001; Wei Ai 002; Xiang Song 003; Danai Koutra (WSDM, 2024)

Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. - Charles Dickens; Edward W. Huang; Aishwarya Reganti; Jiong Zhu; Karthik Subbian; Danai Koutra (WWW (Companion Volume), 2024)

LinkGPT: Teaching Large Language Models To Predict Missing Links. - Zhongmou He; Jing Zhu 005; Shengyi Qian 001; Joyce Chai; Danai Koutra (CoRR, 2024)

Large Generative Graph Models. - Yu Wang 160; Ryan A. Rossi; Namyong Park; Huiyuan Chen; Nesreen K. Ahmed; Puja Trivedi; Franck Dernoncourt; Danai Koutra; Tyler Derr (CoRR, 2024)

Multimodal Graph Benchmark. - Jing Zhu 005; Yuhang Zhou; Shengyi Qian 001; Zhongmou He; Tong Zhao 003; Neil Shah; Danai Koutra (CoRR, 2024)

Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data. - Jiacheng Lin; Kun Qian 018; Haoyu Han; Nurendra Choudhary; Tianxin Wei; Zhongruo Wang; Sahika Genc; Edward W. Huang; Sheng Wang 012; Karthik Subbian; Danai Koutra; Jimeng Sun 001 (CoRR, 2024)

Unified Dense Subgraph Detection: Fast Spectral Theory Based Algorithms. - Wenjie Feng 001; Shenghua Liu; Danai Koutra; Xueqi Cheng (IEEE Trans. Knowl. Data Eng., 2024)

Understanding GNNs and Homophily in Dynamic Node Classification. - Michael Ito; Danai Koutra; Jenna Wiens (AISTATS, 2025)

Learning Laplacian Positional Encodings for Heterophilous Graphs. - Michael Ito; Jiong Zhu; Dexiong Chen; Danai Koutra; Jenna Wiens (AISTATS, 2025)

Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning. - Jing Zhu 005; Yuhang Zhou; Shengyi Qian 001; Zhongmou He; Tong Zhao 003; Neil Shah; Danai Koutra (CVPR, 2025)

Not all Neighbors Agree: Graph Learning Beyond Homophily. - Danai Koutra (GRADES/NDA, 2025)

A Large-scale Training Paradigm for Graph Generative Models. - Yu Wang 160; Ryan A. Rossi; Namyong Park; Huiyuan Chen; Nesreen K. Ahmed; Puja Trivedi; Franck Dernoncourt; Danai Koutra; Tyler Derr (ICLR, 2025)

MAGNET: A Multi-Agent Graph Neural Network for Efficient Bipartite Task Assignment. - Donald Loveland; James Usevitch; Zachary Serlin; Danai Koutra; Rajmonda Caceres (AAMAS, 2025)

Demystifying the Power of Large Language Models in Graph Generation. - Yu Wang 160; Ryan A. Rossi; Namyong Park; Nesreen K. Ahmed; Danai Koutra; Franck Dernoncourt; Tyler Derr (NAACL (Findings), 2025)

Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. - Donald Loveland; Danai Koutra (SDM, 2025)

Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. - Lifu Huang; Danai Koutra; Adithya Kulkarni; Temiloluwa Prioleau; Qingyun Wu; Yujun Yan; Yaoqing Yang; James Zou 001; Dawei Zhou 003 (WWW (Companion Volume), 2025)

Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. - Donald Loveland; Xinyi Wu 003; Tong Zhao 003; Danai Koutra; Neil Shah; Mingxuan Ju (WWW, 2025)

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation. - Jiong Zhu; Aishwarya Reganti; Edward W. Huang; Charles Dickens; Nikhil Rao 001; Karthik Subbian; Danai Koutra (ACM Trans. Knowl. Discov. Data, 2025)

Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics. - Jing Zhu 005; Mingxuan Ju; Yozen Liu; Danai Koutra; Neil Shah; Tong Zhao 003 (CoRR, 2025)

On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. - Donald Loveland; Mingxuan Ju; Tong Zhao 003; Neil Shah; Danai Koutra (CoRR, 2025)