Fast Approximations of Betweenness Centrality with Graph Neural Networks

被引:19
|
作者
Maurya, Sunil Kumar [1 ]
Liu, Xin [2 ]
Murata, Tsuyoshi [1 ]
机构
[1] Tokyo Inst Technol, Tokyo, Japan
[2] AIST, AIRC, Tokyo, Japan
来源
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) | 2019年
关键词
D O I
10.1145/3357384.3358080
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Betweenness centrality is an important measure to find out influential nodes in networks in terms of information spread and connectivity. However, the exact calculation of betweenness centrality is computationally expensive. Although researchers have proposed approximation methods, they are either less efficient, or suboptimal, or both. In this paper, we present a Graph Neural Network(GNN) based inductive framework which uses constrained message passing of node features to approximate betweenness centrality. As far as we know, we are the first to propose a GNN based model to accomplish this task. We demonstrate that our approach dramatically outperforms current techniques while taking less amount of time through extensive experiments on a series of real-world datasets.
引用
收藏
页码:2149 / 2152
页数:4
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