Publications

Here is a list of my publications. My Google scholar page can be found here

* Denotes equal contribution.

Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju. "Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank", Under Review, 2025

Donald Loveland, Danai Koutra. "Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks", SIAM International Conference on Data Mining, 2025
Donald Loveland, James Usevitch, Zachary Serlin, Danai Koutra, Rajmonda Caceres. "MAGNET: A Multi-Agent Graph Neural Network for Efficient Bipartite Task Assignment", International Conference on Autonomous Agents and Multi-Agent Systems, 2025

Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T Schaub, Danai Koutra. "On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks", Learning on Graphs, 2023, (Spotlight Oral, Top 5%)
Donald Loveland, Rajmonda Caceres. "Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance.", International Conference on Complex Networks and Their Applications, 2023 (Oral)

Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T Schaub, Danai Koutra. "On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods", KDD Workshop on Deep Learning on Graphs, 2022
Jiong Zhu, Junchen Jin, Donald Loveland, Michael T Schaub, and Danai Koutra. "How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications.", In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022

Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, Yong Han. "Reliable Graph Neural Network Explanations Through Adversarial Training", ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2021
Phan Nguyen*, Donald Loveland*, Joanne T Kim, Piyush Karande, Anna M Hiszpanski, Yong Han. "Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning", Journal of Chemical Information and Modeling, 2021

Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M Hiszpanski, Yong Han. "Automated Identification of Molecular Crystals’ Packing Motifs", Journal of Chemical Information and Modeling, 2020
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M Hiszpanski, Emily Robertson, Donald Loveland, Yong Han. "Actionable attribution maps for scientific machine learning", ICML Workshop on ML Interpretability for Scientific Discovery, 2020
Brian Gallagher, Matthew Rever, Donald Loveland, T Nathan Mundhenk, Brock Beauchamp, Emily Robertson, Golam G Jaman, Anna M. Hiszpanski, Yong Han. "Predicting Compressive Strength of Consolidated Molecular Solids using Computer Vision and Deep Learning", Materials & Design, 2020

Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han. "Generative Counterfactual Introspection for Explainable Deep Learning", IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019

Vardha N Bennert, Donald Loveland, Edward Donohue, Maren Cosens, Sean Lewis, S Komossa, Tommaso Treu, Matthew A Malkan, Nathan Milgram, Kelsi Flatland, Matthew W Auger, Daeseong Park, Mariana S Lazarova, "Studying the [OIII]λ 5007Å emission-line width in a sample of ∼ 80 local active galaxies: a surrogate for σ⋆?", Monthly Notices of the Royal Astronomical Society, 2018