69 citations · 113 across the 7 of their papers we have counts for
Showing 2021Show all
3 papers · 1 filter
cs.LG2021★ 1 cited
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
Jun Hu, Shengsheng Qian, Quan Fang +1
Graph Neural Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structure-aware features. Existing GNN research designs various prop…
cs.IR2021★ 4 cited
GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
Desheng Cai, Jun Hu, Quan Zhao +3
In this paper, we present GRecX, an open-source TensorFlow framework for benchmarking GNN-based recommendation models in an efficient and unified way. GRecX consists of core librar…
cs.LG2021★ 9 cited
Efficient Graph Deep Learning in TensorFlow with tf_geometric
Jun Hu, Shengsheng Qian, Quan Fang +4
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries f…