9 citations · 35 across the 11 of their papers we have counts for
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cs.LG2021★ 5 cited
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows
Junchi Yu, Tingyang Xu, Ran He
As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparen…
cs.LG2021★ 5 cited
Improving Subgraph Recognition with Variational Graph Information Bottleneck
Junchi Yu, Jie Cao, Ran He
Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property. It can be formulated by optimizing Graph Information B…
cs.LG2021★ 7 cited
Recognizing Predictive Substructures with Subgraph Information Bottleneck
Junchi Yu, Tingyang Xu, Yu Rong +3
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack…