Danai Koutra

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 143

Top Venues

Publications

Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra
LinkGPT: Teaching Large Language Models To Predict Missing Links. CoRR (2024)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR (2024)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR (2024)
Charles Dickens, Edward W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. WWW (Companion Volume) (2024)
Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices. WSDM (2024)
Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. SIGIR (2024)
Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation. CoRR (2024)
Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra
Multimodal Graph Benchmark. CoRR (2024)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
On Estimating Link Prediction Uncertainty Using Stochastic Centering. ICASSP (2024)
Wenjie Feng, Shenghua Liu, Danai Koutra, Xueqi Cheng
Unified Dense Subgraph Detection: Fast Spectral Theory Based Algorithms. IEEE Trans. Knowl. Data Eng. 36 (3) (2024)
Yu Wang, Ryan A. Rossi, Namyong Park, Huiyuan Chen, Nesreen K. Ahmed, Puja Trivedi, Franck Dernoncourt, Danai Koutra, Tyler Derr
Large Generative Graph Models. CoRR (2024)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look at Scoring Functions and Generalization Prediction. CoRR (2023)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. ICLR (2023)
Jiong Zhu, Aishwarya Reganti, Edward W. Huang, Charles Dickens, Nikhil Rao, Karthik Subbian, Danai Koutra
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation. CoRR (2023)
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR (2023)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR (2023)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. CoRR (2023)
Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. CoRR (2023)
Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra
SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks. CoRR (2023)
Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng
A Provable Framework of Learning Graph Embeddings via Summarization. AAAI (2023)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look At Scoring Functions And Generalization Prediction. ICASSP (2023)
Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, Danai Koutra
Heterophily and Graph Neural Networks: Past, Present and Future. IEEE Data Eng. Bull. 46 (2) (2023)
Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. CoRR (2023)
Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra
Leveraging Graph Diffusion Models for Network Refinement Tasks. CoRR (2023)
Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks. KDD (2023)
Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). KDD (2023)
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG (2023)
Charles Dickens, Eddie W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. CoRR (2023)
Danai Koutra, Henning Meyerhenke, Ilya Safro, Fabian Brandt-Tumescheit
Scalable Graph Mining and Learning (Dagstuhl Seminar 23491). Dagstuhl Reports 13 (12) (2023)
Yujun Yan, Gaotang Li, Danai Koutra
Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective. CoRR (2023)
Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra
Leveraging the Graph Structure of Neural Network Training Dynamics. CIKM (2022)
Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra
On Generalizing Static Node Embedding to Dynamic Settings. WSDM (2022)
Jing Zhu, Danai Koutra, Mark Heimann
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CIKM (2022)
Caleb Belth, Alican Büyükçakir, Danai Koutra
A hidden challenge of link prediction: which pairs to check? Knowl. Inf. Syst. 64 (3) (2022)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. CoRR (2022)
Jing Zhu, Danai Koutra, Mark Heimann
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. CoRR (2022)
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS (2022)
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. ICDM (2022)
Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
Toward Understanding and Evaluating Structural Node Embeddings. ACM Trans. Knowl. Discov. Data 16 (3) (2022)
Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lye, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He
Accepted Tutorials at The Web Conference 2022. WWW (Companion Volume) (2022)
Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T. Schaub, Danai Koutra
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR (2022)
Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng
Learning node embeddings via summary graphs: a brief theoretical analysis. CoRR (2022)
Ekdeep Singh Lubana, Puja Trivedi, Danai Koutra, Robert P. Dick
How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation. CoLLAs (2022)
Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra
How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. KDD (2022)
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR (2022)
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. WWW (2022)
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. CoRR (2021)
Jiong Zhu, Junchen Jin, Michael T. Schaub, Danai Koutra
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. CoRR (2021)
Tara Safavi, Danai Koutra
Relational World Knowledge Representation in Contextual Language Models: A Review. EMNLP (1) (2021)
Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra
Refining Network Alignment to Improve Matched Neighborhood Consistency. SDM (2021)
Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. SDM (2021)
Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
Towards Understanding and Evaluating Structural Node Embeddings. CoRR (2021)
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. CoRR (2021)
Tara Safavi, Danai Koutra
Relational world knowledge representation in contextual language models: A review. CoRR (2021)
Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra
Refining Network Alignment to Improve Matched Neighborhood Consistency. CoRR (2021)
Caleb Belth, Alican Büyükçakir, Danai Koutra
A Hidden Challenge of Link Prediction: Which Pairs to Check? CoRR (2021)
Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. CoRR (2021)
Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra
Convolutional Neural Network Dynamics: A Graph Perspective. CoRR (2021)
Tara Safavi, Jing Zhu, Danai Koutra
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases. EMNLP (1) (2021)
Di Jin, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Danai Koutra
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. CoRR (2021)
Nishil Talati, Di Jin, Haojie Ye, Ajay Brahmakshatriya, Ganesh S. Dasika, Saman P. Amarasinghe, Trevor N. Mudge, Danai Koutra, Ronald G. Dreslinski
A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning. IISWC (2021)
Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra
Graph Neural Networks with Heterophily. AAAI (2021)
Di Jin, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Danai Koutra
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. Proc. VLDB Endow. 15 (3) (2021)
Danai Koutra
The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability. Proc. VLDB Endow. 14 (13) (2021)
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
Consistent Network Alignment with Node Embedding. CoRR (2020)
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. NeurIPS (2020)
Jiong Zhu, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra
Graph Neural Networks with Heterophily. CoRR (2020)
Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra
Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. DSAA (2020)
Tara Safavi, Danai Koutra
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. CoRR (2020)
Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra
Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data. CoRR (2020)
Tara Safavi, Danai Koutra, Edgar Meij
Improving the Utility of Knowledge Graph Embeddings with Calibration. CoRR (2020)
Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng
SpecGreedy: Unified Dense Subgraph Detection. ECML/PKDD (1) (2020)
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. CIKM (2020)
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Neural Execution Engines: Learning to Execute Subroutines. NeurIPS (2020)
Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. 27 (12) (2020)
Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CoRR (2020)
Tara Safavi, Adam Fourney, Robert Sim, Marcin Juraszek, Shane Williams, Ned Friend, Danai Koutra, Paul N. Bennett
Toward Activity Discovery in the Personal Web. WSDM (2020)
Caleb Belth, Xinyi Zheng, Danai Koutra
Mining Persistent Activity in Continually Evolving Networks. KDD (2020)
Scott McMillan, Manoj Kumar, Danai Koutra, Mahantesh Halappanavar, Tim Mattson, Antonino Tumeo
Message from the workshop chairs. IPDPS Workshops (2020)
Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications. ACM Trans. Knowl. Discov. Data 14 (5) (2020)
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
Generalizing Graph Neural Networks Beyond Homophily. CoRR (2020)
Tara Safavi, Danai Koutra
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. EMNLP (1) (2020)
Tara Safavi, Danai Koutra, Edgar Meij
Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction. EMNLP (1) (2020)
Caleb Belth, Alican Büyükçakir, Danai Koutra
A Hidden Challenge of Link Prediction: Which Pairs to Check? ICDM (2020)
Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. CoRR (2020)
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Neural Execution Engines: Learning to Execute Subroutines. CoRR (2020)
Tara Safavi, Danai Koutra
Generating Negative Commonsense Knowledge. CoRR (2020)
Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
G-CREWE: Graph CompREssion With Embedding for Network Alignment. CIKM (2020)
Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. WWW (2020)
Saba A. Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam
t-PINE: tensor-based predictable and interpretable node embeddings. Soc. Netw. Anal. Min. 10 (1) (2020)
Caleb Belth, Xinyi Zheng, Danai Koutra
Mining Persistent Activity in Continually Evolving Networks. CoRR (2020)
Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra
From Static to Dynamic Node Embeddings. CoRR (2020)
Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Müller, Danai Koutra
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket. ICDM (2019)
Mark Heimann, Tara Safavi, Danai Koutra
Distribution of Node Embeddings as Multiresolution Features for Graphs. ICDM (2019)
Di Jin, Ryan A. Rossi, Eunyee Koh, Sungchul Kim, Anup Rao, Danai Koutra
Latent Network Summarization: Bridging Network Embedding and Summarization. KDD (2019)
Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. CoRR (2019)
Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra
node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching. ECML/PKDD (1) (2019)
Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sekhar Sripada, Danai Koutra
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data. KDD (2019)
Sang Won Lee, Aaron Willette, Danai Koutra, Walter S. Lasecki
The Effect of Social Interaction on Facilitating Audience Participation in a Live Music Performance. Creativity & Cognition (2019)
Tara Safavi, Chandra Sekhar Sripada, Danai Koutra
Fast network discovery on sequence data via time-aware hashing. Knowl. Inf. Syst. 61 (2) (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Danai Koutra