Xiang Song

According to our database, Xiang Song authored at least 34 papers between 2020 and 2024.

Collaborative distances:

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

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, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang

Proc. VLDB Endow., February, 2024

Hierarchical Compression of Text-Rich Graphs via Large Language Models.

Shichang Zhang, Da Zheng, Jiani Zhang, Qi Zhu, Xiang Song, Soji Adeshina, Christos Faloutsos, George Karypis, Yizhou Sun

CoRR, 2024

GraphStorm: all-in-one graph machine learning framework for industry applications.

Da Zheng, Xiang Song, Qi Zhu, Jiani Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis

CoRR, 2024

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning.

Qi Zhu, Da Zheng, Xiang Song, Shichang Zhang, Bowen Jin, Yizhou Sun, George Karypis

CoRR, 2024

KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion.

Tengfei Ma, Xiang Song, Wen Tao, Mufei Li, Jiani Zhang, Xiaoqin Pan, Jianxin Lin, Bosheng Song, Xiangxiang Zeng

CoRR, 2024

Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices.

Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning.

Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications.

Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

NetInfoF Framework: Measuring and Exploiting Network Usable Information.

Meng-Chieh Lee, Haiyang Yu, Jian Zhang, Vassilis N. Ioannidis, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures.

Kun Wu, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei Hwu

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023

SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks.

Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra

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, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang

CoRR, 2023

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks.

Kun Wu, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei W. Hwu

CoRR, 2023

PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction.

Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun

Proceedings of the ACM Web Conference 2023, 2023

DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training.

Hongkuan Zhou, Da Zheng, Xiang Song, George Karypis, Viktor K. Prasanna

Proceedings of the International Conference for High Performance Computing, 2023

DSP: Efficient GNN Training with Multiple GPUs.

Zhenkun Cai, Qihui Zhou, Xiao Yan, Da Zheng, Xiang Song, Chenguang Zheng, James Cheng, George Karypis

Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, 2023

GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes.

Jian Zhang, Da Zheng, Xiang Song, Theodore Vasiloudis, Israt Nisa, Jim Lu

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications.

Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N. Ioannidis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Belinda Zeng, Trishul Chilimbi

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 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, Xiang Song, Wen-Mei Hwu

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

On the Initialization of Graph Neural Networks.

Jiahang Li, Yakun Song, Xiang Song, David Wipf

Proceedings of the International Conference on Machine Learning, 2023

2022

TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs.

Hongkuan Zhou, Da Zheng, Israt Nisa, Vassilis N. Ioannidis, Xiang Song, George Karypis

Proc. VLDB Endow., 2022

Efficient and effective training of language and graph neural network models.

Vassilis N. Ioannidis, Xiang Song, Da Zheng, Houyu Zhang, Jun Ma, Yi Xu, Belinda Zeng, Trishul Chilimbi, George Karypis

CoRR, 2022

ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases.

Bo He, Xiang Song, Vincent Gao, Christos Faloutsos

CoRR, 2022

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs.

Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, George Karypis

CoRR, 2022

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs.

Da Zheng, Xiang Song, Chengru Yang, Dominique LaSalle, George Karypis

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

Graph Neural Network Training and Data Tiering.

Seungwon Min, Kun Wu, Mert Hidayetoglu, Jinjun Xiong, Xiang Song, Wen-Mei Hwu

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

2021

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs.

Da Zheng, Xiang Song, Chengru Yang, Dominique LaSalle, Qidong Su, Minjie Wang, Chao Ma, George Karypis

CoRR, 2021

Network In Graph Neural Network.

Xiang Song, Runjie Ma, Jiahang Li, Muhan Zhang, David Paul Wipf

CoRR, 2021

Graph Neural Network Training with Data Tiering.

Seungwon Min, Kun Wu, Mert Hidayetoglu, Jinjun Xiong, Xiang Song, Wen-mei W. Hwu

CoRR, 2021

Scalable Graph Neural Networks with Deep Graph Library.

Da Zheng, Minjie Wang, Quan Gan, Xiang Song, Zheng Zhang, George Karypis

Proceedings of the WSDM '21, 2021

2020

COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature.

Colby Wise, Vassilis N. Ioannidis, Miguel Romero Calvo, Xiang Song, George Price, Ninad Kulkarni, Ryan Brand, Parminder Bhatia, George Karypis

CoRR, 2020

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning.

Xiangxiang Zeng, Xiang Song, Tengfei Ma, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang, George Karypis, Feixiong Cheng

CoRR, 2020

DGL-KE: Training Knowledge Graph Embeddings at Scale.

Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs.

Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, George Karypis

Proceedings of the 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, 2020