Network Node Completion Based on Graph Convolutional Network

被引：0

作者：

Liu C. ^{[1
]}

Li Z. ^{[1
]}

Zhou L. ^{[1
]}

机构：

[1] Business School, University of Shanghai for Science and Technology, Shanghai

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Deep Learning; Graph Convolutional Network; Network Completion; Node Completion;

D O I：

10.16451/j.cnki.issn1003-6059.202106005

中图分类号：

学科分类号：

摘要：

Aiming at the incomplete network data and missing nodes in graph data structure, a network node completion algorithm based on graph convolutional network is proposed. Firstly, the observed network is sampled in pairs to construct the closed subgraph and feature matrix of the target node pair. Then, the graph convolutional neural network is employed to extract the representation vectors of subgraphs and their feature matrices for two purposes. One is to infer whether there are missing nodes between target node pairs of each subgraph, and the other is whether the missing nodes between different target node pairs are the same node. Finally, experiments on real network datasets and artificially generated network datasets show that the proposed model can solve the problem of network completion well and recover the network even when half of the nodes in the network are missing. © 2021, Science Press. All right reserved.

引用

页码：532 / 540

页数：8

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