Donald Loveland

According to our database, Donald Loveland authored at least 18 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective.

Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Danai Koutra

CoRR, May, 2025

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems.

Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu, Xin Chen, Leonardo Neves, Ali Jadbabaie, Mingxuan Ju, Neil Shah, Tong Zhao

Proceedings of the ACM on Web Conference 2025, 2025

Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank.

Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju

Proceedings of the ACM on Web Conference 2025, 2025

Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks.

Donald Loveland, Danai Koutra

Proceedings of the 2025 SIAM International Conference on Data Mining, 2025

MAGNET: A Multi-Agent Graph Neural Network for Efficient Bipartite Task Assignment.

Donald Loveland, James Usevitch, Zachary Serlin, Danai Koutra, Rajmonda Caceres

Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, 2025

2024

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems.

Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu, Xin Chen, Leonardo Neves, Ali Jadbabaie, Clark Mingxuan Ju, Neil Shah, Tong Zhao

CoRR, 2024

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

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance.

Donald Loveland, Rajmonda Caceres

Proceedings of the Complex Networks & Their Applications XII, 2023

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

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning.

Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong

CoRR, 2022

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing.

Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, Yiyang Lu

CoRR, 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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.

Phan Nguyen, Donald Loveland, Joanne Taery Kim, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han

J. Chem. Inf. Model., 2021

Reliable Graph Neural Network Explanations Through Adversarial Training.

Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han

CoRR, 2021

2020

Automated Identification of Molecular Crystals' Packing Motifs.

Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han

J. Chem. Inf. Model., 2020

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design.

Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han

CoRR, 2020

Actionable Attribution Maps for Scientific Machine Learning.

Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han

CoRR, 2020

2019

Generative Counterfactual Introspection for Explainable Deep Learning.

Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han

Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing, 2019