Koutra, Danai

25 publications

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

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

CVPR 2025 Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra

NeurIPS 2025 Random Search Neural Networks for Efficient and Expressive Graph Learning Michael Ito, Danai Koutra, Jenna Wiens

AISTATS 2025 Understanding GNNs and Homophily in Dynamic Node Classification Michael Ito, Danai Koutra, Jenna Wiens

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

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

NeurIPSW 2024 LinkGPT: Teaching Large Language Models to Predict Missing Links Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra

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

ICLR 2023 A Closer Look at Model Adaptation Using Feature Distortion and Simplicity Bias Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan

AAAI 2023 A Provable Framework of Learning Graph Embeddings via Summarization Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng

NeurIPSW 2023 Estimating Epistemic Uncertainty of Graph Neural Networks Using Stochastic Centering Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan

LoG 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

ICMLW 2023 Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation Jiong Zhu, Aishwarya Naresh Reganti, Edward W Huang, Charles Andrew Dickens, Nikhil Rao, Karthik Subbian, Danai Koutra

NeurIPSW 2022 A Closer Look at Model Adaptation Using Feature Distortion and Simplicity Bias Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan

NeurIPS 2022 Analyzing Data-Centric Properties for Graph Contrastive Learning Puja Trivedi, Ekdeep S Lubana, Mark Heimann, Danai Koutra, Jayaraman Thiagarajan

CoLLAs 2022 How Do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation Ekdeep Singh Lubana, Puja Trivedi, Danai Koutra, Robert Dick

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

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

ICLR 2020 Neural Execution Engines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

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

ECML-PKDD 2020 SpecGreedy: Unified Dense Subgraph Detection Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng

MLJ 2019 Collaborative Topic Regression for Predicting Topic-Based Social Influence Asso Hamzehei, Raymond K. Wong, Danai Koutra, Fang Chen

ECML-PKDD 2019 Node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra

ECML-PKDD 2011 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