Graph Feature Gating Networks

• URL: http://arxiv.org/abs/2105.04493v1
• Date: Mon, 10 May 2021 16:33:58 GMT
• Title: Graph Feature Gating Networks
• Authors: Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang
• Abstract summary: We propose a general graph feature gating network (GFGN) based on the graph signal denoising problem. We also introduce three graph filters under GFGN to allow different levels of contributions from feature dimensions.
• Score: 31.20878472589719
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where the node representations are updated by aggregating and transforming the information from the neighborhood. Meanwhile, they adopt the same strategy in aggregating the information from different feature dimensions. However, suggested by social dimension theory and spectral embedding, there are potential benefits to treat the dimensions differently during the aggregation process. In this work, we investigate to enable heterogeneous contributions of feature dimensions in GNNs. In particular, we propose a general graph feature gating network (GFGN) based on the graph signal denoising problem and then correspondingly introduce three graph filters under GFGN to allow different levels of contributions from feature dimensions. Extensive experiments on various real-world datasets demonstrate the effectiveness and robustness of the proposed frameworks.

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