4 citations · 4 across the 3 of their papers we have counts for
7 papers
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours
Yuxin Yang, Hongkuan Zhou, Rajgopal Kannan +1
Temporal Graph Neural Networks (TGNNs) have emerged as powerful tools for modeling dynamic interactions across various domains. The design space of TGNNs is notably complex, given…
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA
Hongkuan Zhou, Bingyi Zhang, Rajgopal Kannan +2
Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs. The generated temporal node embeddings ou…
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings
Hongkuan Zhou, James Orme-Rogers, Rajgopal Kannan +1
Temporal Knowledge Graphs store events in the form of subjects, relations, objects, and timestamps which are often represented by dynamic heterogeneous graphs. Event forecasting is…
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng +2
Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications. However, due to the high computation complexity of GNN inferen…
Accurate, Efficient and Scalable Training of Graph Neural Networks
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform tr…
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer…