DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. - Da Zheng; Chao Ma 025; Minjie Wang; Jinjing Zhou; Qidong Su; Xiang Song 003; Quan Gan; Zheng Zhang 001; George Karypis (IA3@SC, 2020)

DGL-KE: Training Knowledge Graph Embeddings at Scale. - Da Zheng; Xiang Song 003; Chao Ma 025; Zeyuan Tan; Zihao Ye 001; Jin Dong; Hao Xiong; Zheng Zhang 001; George Karypis (SIGIR, 2020)

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning. - Xiangxiang Zeng; Xiang Song 003; Tengfei Ma 002; Xiaoqin Pan; Yadi Zhou; Yuan Hou; Zheng Zhang 001; George Karypis; Feixiong Cheng (CoRR, 2020)

COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature. - Colby Wise; Vassilis N. Ioannidis; Miguel Romero Calvo; Xiang Song 003; George Price; Ninad Kulkarni; Ryan Brand; Parminder Bhatia; George Karypis (CoRR, 2020)

Scalable Graph Neural Networks with Deep Graph Library. - Da Zheng; Minjie Wang; Quan Gan; Xiang Song 003; Zheng Zhang 001; George Karypis (WSDM, 2021)

Graph Neural Network Training with Data Tiering. - Seungwon Min; Kun Wu 002; Mert Hidayetoglu; Jinjun Xiong; Xiang Song 003; Wen-mei W. Hwu (CoRR, 2021)

Network In Graph Neural Network. - Xiang Song 003; Runjie Ma; Jiahang Li 002; Muhan Zhang; David Paul Wipf (CoRR, 2021)

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs. - Da Zheng; Xiang Song 003; Chengru Yang; Dominique LaSalle; Qidong Su; Minjie Wang; Chao Ma 025; George Karypis (CoRR, 2021)

Graph Neural Network Training and Data Tiering. - Seungwon Min; Kun Wu 002; Mert Hidayetoglu; Jinjun Xiong; Xiang Song 003; Wen-Mei Hwu (KDD, 2022)

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs. - Da Zheng; Xiang Song 003; Chengru Yang; Dominique LaSalle; George Karypis (KDD, 2022)

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs. - Hongkuan Zhou; Da Zheng; Israt Nisa; Vasileios Ioannidis; Xiang Song 003; George Karypis (CoRR, 2022)

ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases. - Bo He; Xiang Song 003; Vincent Gao; Christos Faloutsos (CoRR, 2022)

Efficient and effective training of language and graph neural network models. - Vassilis N. Ioannidis; Xiang Song 003; Da Zheng; Houyu Zhang; Jun Ma 029; Yi Xu 011; Belinda Zeng; Trishul Chilimbi; George Karypis (CoRR, 2022)

TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs. - Hongkuan Zhou; Da Zheng; Israt Nisa; Vassilis N. Ioannidis; Xiang Song 003; George Karypis (Proc. VLDB Endow., 2022)

On the Initialization of Graph Neural Networks. - Jiahang Li 002; Yakun Song; Xiang Song 003; David Wipf (ICML, 2023)

IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research. - Arpandeep Khatua; Vikram Sharma Mailthody; Bhagyashree Taleka; Tengfei Ma 001; Xiang Song 003; Wen-Mei Hwu (KDD, 2023)

Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications. - Han Xie; Da Zheng; Jun Ma 029; Houyu Zhang; Vassilis N. Ioannidis; Xiang Song 003; Qing Ping; Sheng Wang 012; Carl Yang 001; Yi Xu 011; Belinda Zeng; Trishul Chilimbi (KDD, 2023)

GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes. - Jian Zhang; Da Zheng; Xiang Song 003; Theodore Vasiloudis; Israt Nisa; Jim Lu (KDD, 2023)

DSP: Efficient GNN Training with Multiple GPUs. - Zhenkun Cai; Qihui Zhou; Xiao Yan 002; Da Zheng; Xiang Song 003; Chenguang Zheng; James Cheng; George Karypis (PPoPP, 2023)

DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training. - Hongkuan Zhou; Da Zheng; Xiang Song 003; George Karypis; Viktor K. Prasanna (SC, 2023)

PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction. - Shichang Zhang; Jiani Zhang 003; Xiang Song 003; Soji Adeshina; Da Zheng; Christos Faloutsos; Yizhou Sun (WWW, 2023)

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks. - Kun Wu 002; Mert Hidayetoglu; Xiang Song 003; Sitao Huang; Da Zheng; Israt Nisa; Wen-Mei W. Hwu (CoRR, 2023)

ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training. - Kezhao Huang; Haitian Jiang; Minjie Wang; Guangxuan Xiao; David Wipf; Xiang Song 003; Quan Gan; Zengfeng Huang; Jidong Zhai; Zheng Zhang 001 (CoRR, 2023)

SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks. - Jing Zhu 005; Yuhang Zhou; Vassilis N. Ioannidis; Shengyi Qian 001; Wei Ai 002; Xiang Song 003; Danai Koutra (CoRR, 2023)

Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures. - Kun Wu 002; Mert Hidayetoglu; Xiang Song 003; Sitao Huang; Da Zheng; Israt Nisa; Wen-Mei Hwu (ASPLOS (3), 2024)

NetInfoF Framework: Measuring and Exploiting Network Usable Information. - Meng-Chieh Lee; Haiyang Yu; Jian Zhang; Vassilis N. Ioannidis; Xiang Song 003; Soji Adeshina; Da Zheng; Christos Faloutsos (ICLR, 2024)

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. - Da Zheng; Xiang Song 003; Qi Zhu 008; Jian Zhang; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang 002; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; et al. (KDD, 2024)

TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. - Jing Zhu 005; Xiang Song 003; Vassilis N. Ioannidis; Danai Koutra; Christos Faloutsos (SIGIR, 2024)

Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices. - Jing Zhu 005; Yuhang Zhou; Vassilis N. Ioannidis; Shengyi Qian 001; Wei Ai 002; Xiang Song 003; Danai Koutra (WSDM, 2024)

KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion. - Tengfei Ma 002; Xiang Song 003; Wen Tao; Mufei Li; Jiani Zhang 003; Xiaoqin Pan; Jianxin Lin; Bosheng Song; Xiangxiang Zeng (CoRR, 2024)

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning. - Qi Zhu 008; Da Zheng; Xiang Song 003; Shichang Zhang; Bowen Jin; Yizhou Sun; George Karypis (CoRR, 2024)

GraphStorm: all-in-one graph machine learning framework for industry applications. - Da Zheng; Xiang Song 003; Qi Zhu 008; Jiani Zhang 003; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang 002; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; et al. (CoRR, 2024)

Hierarchical Compression of Text-Rich Graphs via Large Language Models. - Shichang Zhang; Da Zheng; Jiani Zhang 003; Qi Zhu 008; Xiang Song 003; Soji Adeshina; Christos Faloutsos; George Karypis; Yizhou Sun (CoRR, 2024)

FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training. - Kezhao Huang; Haitian Jiang; Minjie Wang; Guangxuan Xiao; David Wipf; Xiang Song 003; Quan Gan; Zengfeng Huang; Jidong Zhai; Zheng Zhang 001 (Proc. VLDB Endow., 2024)

Towards Synergistic Path-based Explanations for Knowledge Graph Completion: Exploration and Evaluation. - Tengfei Ma 002; Xiang Song 003; Wen Tao; Mufei Li; Jiani Zhang 003; Xiaoqin Pan; Yijun Wang 002; Bosheng Song; Xiangxiang Zeng (ICLR, 2025)

AutoG: Towards automatic graph construction from tabular data. - Zhikai Chen; Han Xie; Jian Zhang; Xiang Song 003; Jiliang Tang; Huzefa Rangwala; George Karypis (ICLR, 2025)

Spectro-Riemannian Graph Neural Networks. - Karish Grover; Haiyang Yu; Xiang Song 003; Qi Zhu 008; Han Xie; Vassilis N. Ioannidis; Christos Faloutsos (ICLR, 2025)

Adaptive Parallel Training for Graph Neural Networks. - Kaihao Ma; Renjie Liu 001; Xiao Yan 002; Zhenkun Cai; Xiang Song 003; Minjie Wang; Yichao Li; James Cheng (PPoPP, 2025)