69 citations · 83 across the 4 of their papers we have counts for
4 papers
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Jun Hu, Bryan Hooi, Shengsheng Qian +2
Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. Howeve…
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…
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…
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…