Kavli Affiliate: Xiang Zhang
| First 5 Authors: Tianxiang Zhao, Xiang Zhang, Suhang Wang, ,
| Summary:
Edges in real-world graphs are typically formed by a variety of factors and
carry diverse relation semantics. For example, connections in a social network
could indicate friendship, being colleagues, or living in the same
neighborhood. However, these latent factors are usually concealed behind mere
edge existence due to the data collection and graph formation processes.
Despite rapid developments in graph learning over these years, most models take
a holistic approach and treat all edges as equal. One major difficulty in
disentangling edges is the lack of explicit supervisions. In this work, with
close examination of edge patterns, we propose three heuristics and design
three corresponding pretext tasks to guide the automatic edge disentanglement.
Concretely, these self-supervision tasks are enforced on a designed edge
disentanglement module to be trained jointly with the downstream node
classification task to encourage automatic edge disentanglement. Channels of
the disentanglement module are expected to capture distinguishable relations
and neighborhood interactions, and outputs from them are aggregated as node
representations. The proposed DisGNN is easy to be incorporated with various
neural architectures, and we conduct experiments on $6$ real-world datasets.
Empirical results show that it can achieve significant performance gains.
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