Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159519
Title: Info2vec: an aggregative representation method in multi-layer and heterogeneous networks
Authors: Yang, Guoli
Kang, Yuanji
Zhu, Xianqiang
Zhu, Cheng
Xiao, Gaoxi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Yang, G., Kang, Y., Zhu, X., Zhu, C. & Xiao, G. (2021). Info2vec: an aggregative representation method in multi-layer and heterogeneous networks. Information Sciences, 574, 444-460. https://dx.doi.org/10.1016/j.ins.2021.06.013
Project: RG19/20
Journal: Information Sciences
Abstract: 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.
URI: https://hdl.handle.net/10356/159519
ISSN: 0020-0255
DOI: 10.1016/j.ins.2021.06.013
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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