Info2vec: an aggregative representation method in multi-layer and heterogeneous networks

Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi...

全面介紹

Saved in:
書目詳細資料
Main Authors: Yang, Guoli, Kang, Yuanji, Zhu, Xianqiang, Zhu, Cheng, Xiao, Gaoxi
其他作者: School of Electrical and Electronic Engineering
格式: Article
語言:English
出版: 2022
主題:
在線閱讀:https://hdl.handle.net/10356/159519
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結:Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi-layer and heterogeneous networks. Specifically, an aggregation network is firstly obtained by graph transformation, generating potential information links based on the network structure on different layers. A comprehensive measurement of the similarity between different nodes in the aggregation network is then carried out by aggregating the information of nodes’ identities of structure, nearness and attributes etc. Based on the comprehensive similarity values the nodes have, a context graph can be generated using a simple edge percolation method, which provides a basis facilitating some important downstream work such as classification, clustering and prediction etc. We demonstrate the effectiveness of the new framework in identifying subnetworks in a cyberspace network, where it significantly outperforms all the existing baselines.