An Efficient Distributed Graph Engine for Deep Learning on Graphs. - Gangda Deng; Ömer Faruk Akgül; Hongkuan Zhou; Hanqing Zeng; Yinglong Xia; Jianbo Li; Viktor K. Prasanna (SC Workshops, 2023)

PairGNNs: enabling graph neural networks with pair-based view. - Chenhuan Yu; Gangda Deng; Ning Gui (Neural Comput. Appl., 2023)

A Unified CPU-GPU Protocol for GNN Training. - Yi-Chien Lin; Gangda Deng; Viktor K. Prasanna (CF, 2024)

TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning. - Gangda Deng; Hongkuan Zhou; Hanqing Zeng; Yinglong Xia; Christopher Leung; Jianbo Li; Rajgopal Kannan; Viktor K. Prasanna (IPDPS, 2024)

Learning Personalized Scoping for Graph Neural Networks under Heterophily. - Gangda Deng; Hongkuan Zhou; Rajgopal Kannan; Viktor K. Prasanna (CoRR, 2024)

LocAgent: Graph-Guided LLM Agents for Code Localization. - Zhaoling Chen; Robert Tang; Gangda Deng; Fang Wu; Jialong Wu 010; Zhiwei Jiang; Viktor K. Prasanna; Arman Cohan; Xingyao Wang 002 (ACL (1), 2025)

AP Selection in Uplink Cell-Free Massive MIMO: An Unsupervised Heterogeneous GNN Approach. - Yuhong Liu; Gangda Deng; Hongkuan Zhou; Cauligi S. Raghavendra; Rajgopal Kannan; Ananthram Swami; Viktor K. Prasanna (WCNC, 2025)

LocAgent: Graph-Guided LLM Agents for Code Localization. - Zhaoling Chen; Xiangru Tang; Gangda Deng; Fang Wu; Jialong Wu 010; Zhiwei Jiang; Viktor K. Prasanna; Arman Cohan; Xingyao Wang 002 (CoRR, 2025)