Neighbor Enhanced Graph Convolutional Networks for Node Classification
and Recommendation
- URL: http://arxiv.org/abs/2203.16097v1
- Date: Wed, 30 Mar 2022 06:54:28 GMT
- Title: Neighbor Enhanced Graph Convolutional Networks for Node Classification
and Recommendation
- Authors: Hao Chen, Zhong Huang, Yue Xu, Zengde Deng, Feiran Huang, Peng He,
Zhoujun Li
- Abstract summary: We theoretically analyze the affection of the neighbor quality over GCN models' performance.
We propose the Neighbor Enhanced Graph Convolutional Network (NEGCN) framework to boost the performance of existing GCN models.
- Score: 30.179717374489414
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recently proposed Graph Convolutional Networks (GCNs) have achieved
significantly superior performance on various graph-related tasks, such as node
classification and recommendation. However, currently researches on GCN models
usually recursively aggregate the information from all the neighbors or
randomly sampled neighbor subsets, without explicitly identifying whether the
aggregated neighbors provide useful information during the graph convolution.
In this paper, we theoretically analyze the affection of the neighbor quality
over GCN models' performance and propose the Neighbor Enhanced Graph
Convolutional Network (NEGCN) framework to boost the performance of existing
GCN models. Our contribution is three-fold. First, we at the first time propose
the concept of neighbor quality for both node classification and recommendation
tasks in a general theoretical framework. Specifically, for node
classification, we propose three propositions to theoretically analyze how the
neighbor quality affects the node classification performance of GCN models.
Second, based on the three proposed propositions, we introduce the graph
refinement process including specially designed neighbor evaluation methods to
increase the neighbor quality so as to boost both the node classification and
recommendation tasks. Third, we conduct extensive node classification and
recommendation experiments on several benchmark datasets. The experimental
results verify that our proposed NEGCN framework can significantly enhance the
performance for various typical GCN models on both node classification and
recommendation tasks.
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