Unifying community detection and network embedding in attributed networks
| Autor: | Guyu Hu, Pan Zhisong, Yu Ding, Shuaihui Wang, Hao Wei |
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| Rok vydání: | 2021 |
| Předmět: |
Theoretical computer science
Computer science Node (networking) Network embedding 02 engineering and technology Human-Computer Interaction Global information Artificial Intelligence Hardware and Architecture 020204 information systems 0202 electrical engineering electronic engineering information engineering Representation (mathematics) Baseline (configuration management) Software Information Systems |
| Zdroj: | Knowledge and Information Systems. 63:1221-1239 |
| ISSN: | 0219-3116 0219-1377 |
| DOI: | 10.1007/s10115-021-01557-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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