BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network

• URL: http://arxiv.org/abs/2101.05519v1
• Date: Thu, 14 Jan 2021 09:41:00 GMT
• Title: BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
• Authors: Zhixian Chen, Tengfei Ma, Zhihua Jin, Yangqiu Song, Yang Wang
• Abstract summary: Many graph convolutional networks can be regarded as low-pass filters for graph signals. We propose a new model, BiGCN, which represents a graph neural network as a bi-directional low-pass filter. Our model outperforms previous graph neural networks in the tasks of node classification and link prediction on most of the benchmark datasets.
• Score: 35.97496022085212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph convolutional networks have achieved great success on graph-structured data. Many graph convolutional networks can be regarded as low-pass filters for graph signals. In this paper, we propose a new model, BiGCN, which represents a graph neural network as a bi-directional low-pass filter. Specifically, we not only consider the original graph structure information but also the latent correlation between features, thus BiGCN can filter the signals along with both the original graph and a latent feature-connection graph. Our model outperforms previous graph neural networks in the tasks of node classification and link prediction on most of the benchmark datasets, especially when we add noise to the node features.

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