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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 5/2021

17.03.2021 | Regular Paper

Unifying community detection and network embedding in attributed networks

verfasst von: Yu Ding, Hao Wei, Guyu Hu, Zhisong Pan, Shuaihui Wang

Erschienen in: Knowledge and Information Systems | Ausgabe 5/2021

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Abstract

Traditionally, community detection and network embedding are two separate tasks. Network embedding aims to output a vector representation for each node in the network, and community detection aims to find all densely connected groups of nodes and well separate them from others. Most of the existing approaches do community detection and network embedding in a separate manner, and ignore node attributes information, which leads to poor results. In this paper, we propose a novel model that jointly solves the network embedding and community detection problems together. The model can make use of the network local information, the global information and node attributes information collaboratively. We empirically show that by jointly solving these two problems together, the model can greatly improve the ability of community detection, but also learn better network embedding than the advanced baseline methods. We evaluate the proposed model on several datasets, and the experimental results have shown the effectiveness and advancement of our model.
Vorheriger Artikel Expert-driven trace clustering with instance-level constraints
Nächster Artikel Mining discriminative itemsets in data streams using the tilted-time window model

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Fußnoten
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Metadaten
Titel
Unifying community detection and network embedding in attributed networks
verfasst von
Yu Ding
Hao Wei
Guyu Hu
Zhisong Pan
Shuaihui Wang
Publikationsdatum
17.03.2021
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 5/2021
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-021-01557-5

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