Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla. “Class-Imbalanced Learning on Graphs: A Survey.” arXiv. PDF
Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu. “Graph Neural Prompting with Large Language Models.” arXiv. PDF
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla. “Pure Message Passing Can Estimate Common Neighbor for Link Prediction.” arXiv. PDF
Damien Dablain, Geoffrey Siwo, Nitesh V. Chawla. “Generative AI Design and Exploration of Nucleoside Analogs.” arXiv. PDF
Andrés, José Á., Paul Czechowski, Erin K. Grey, Mandana Saebi, Kara J. Andres, Christopher Brown, Nitesh V. Chawla, et al. 2021. “Global Port Survey Quantifies Commercial Shipping’s Effect on Biodiversity.” bioRxiv (Cold Spring Harbor Laboratory), October. PDF
Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla. “Detecting Anomalies in Sequential Data with Higher-order Networks.” arXiv
Yuxiao Dong, Omar Lizardo, and Nitesh V. Chawla. “Do the Young Live in a “Smaller World” Than the Old? Age-Specific Degrees of Separation in a Large-Scale Mobile Communication Network.” arXiv
Yang Yang, Jie Tang, Yuxiao Dong, Qiaozhu Mei, Reid A. Johnson, and Nitesh V. Chawla. “Modeling the Interplay Between Individual Behavior and Network Distributions.” arXiv
2023
Ma, Yihong, Ning Yan, Jiayu Li, Masood Mortazavi, and Nitesh V. Chawla. “HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks.” ArXiv, (2023). PDF
Dablain, Damien, Bartosz Krawczyk, and Nitesh V. Chawla. “DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.” IEEE Transactions on Neural Networks and Learning Systems 34, no. 9 (September 1, 2023): 6390–6404. PDF
Germino, Joe, Nuno Moniz, and Nitesh V. Chawla. “Fairness-Aware Mixture of Experts with Interpretability Budgets.” In Lecture Notes in Computer Science, 341–55, 2023. PDF
Yihong Ma, Md Nafee Al Islam, Jane Cleland-Huang, Nitesh V. Chawla. “Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows.” IEEE Intelligent Systems 38(2): 46-54 (2023). PDF
Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla. “Deep Ensembles for Graphs with Higher-order Dependencies.” Eleventh International Conference on Learning Representations (ICLR 2023)PDF
Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla. “Heterogeneous Graph Masked Autoencoders.” The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI’ 2023)PDF
Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla. “Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency.” The Eleventh International Conference on Learning Representations (ICLR’ 2023)PDF
Jennifer J. Schnur, Nitesh V. Chawla. “Information fusion via symbolic regression: A tutorial in the context of human health .” Information FusionPDF
Mandana Saebi, Bozhao Nan, John Herr, Jessica Wahlers, Zhichun Guo, Andrzej Zurański, Thierry Kogej, Per-Ola Norrby, Abigail Doyle, Olaf Wiest, Nitesh Chawla. “On the Use of Real-World Datasets for Reaction Yield Prediction.” Chemical Science 2023 PDF