A Representation Learning Framework for Property Graphs

1 code implementation • Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 • Yifan Hou, Hongzhi Chen, Changji Li, James Cheng, Ming-Chang Yang

Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, prediction and recommendation.

Graph Embedding Graph Representation Learning +3

Measuring and Improving the Use of Graph Information in Graph Neural Networks

1 code implementation • ICLR 2020 • Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang

Graph neural networks (GNNs) have been widely used for representation learning on graph data.

Representation Learning

BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

no code implementations • 16 Dec 2021 • Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, Chuanxiong Guo

Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.

Graph Property Prediction Node Classification +1

Characterizing Inter-Numerology Interference in Mixed-Numerology OFDM Systems

no code implementations • 28 Sep 2020 • Juquan Mao, Lei Zhang, Stephen McWade, Hongzhi Chen, Pei Xiao

The advent of mixed-numerology multi-carrier (MN-MC) techniques adds flexibilities in supporting heterogeneous services in fifth generation (5G) communication systems and beyond.

Norm-Ranging LSH for Maximum Inner Product Search

1 code implementation • NeurIPS 2018 • Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng

Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee.