GNN

DecGNN: a framework for mapping decoupled GNN models onto CPU-FPGA heterogeneous platform
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
Decoupling the depth and scope of Graph Neural Networks

Old GNN models + New data perspective = Surpassing 1-WL, Avoiding oversmoothing & Overcoming neighborhood explosion
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
Decoupling the depth and scope of Graph Neural Networks
Accelerating large scale real-time GNN inference using channel pruning
Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna
Accurate, efficient and scalable training of Graph Neural Networks
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna
Accelerating large scale GCN inference on FPGA
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
GraphSAINT: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna
GraphSAINT: Graph sampling based inductive learning method
Accurate, efficient and scalable graph embedding
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna