5 citations · 13 across the 7 of their papers we have counts for
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cs.LG2020★ 4 cited
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
Chaojie Ji, Yijia Zheng, Ruxin Wang +2
Molecular optimization, which transforms a given input molecule X into another Y with desirable properties, is essential in molecular drug discovery. The traditional translating ap…
cs.LG2020★ 2 cited
Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks
Chaojie Ji, Ruxin Wang, Hongyan Wu
While graph neural networks (GNNs) have shown a great potential in various tasks on graph, the lack of transparency has hindered understanding how GNNs arrived at its predictions.…
cs.LG2020★ 5 cited
HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs
Chaojie Ji, Ruxin Wang, Rongxiang Zhu +2
Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while maintaining the prediction accuracy deserves attention. The key point is how the algorithm l…