A Late-Fusion Approach to Community Detection in Attributed Networks
| Autor: | Christine Largeron, Shiva Zamani Gharaghooshi, Chang Liu, Osmar R. Zaïane |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Fusion
Exploit Computer science Node (networking) Perspective (graphical) Network structure 02 engineering and technology computer.software_genre Weighting 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030445836 IDA |
| DOI: | 10.1007/978-3-030-44584-3_24 |
| Popis: | The majority of research on community detection in attributed networks follows an “early fusion” approach, in which the structural and attribute information about the network are integrated together as the guide to community detection. In this paper, we propose an approach called late-fusion, which looks at this problem from a different perspective. We first exploit the network structure and node attributes separately to produce two different partitionings. Later on, we combine these two sets of communities via a fusion algorithm, where we introduce a parameter for weighting the importance given to each type of information: node connections and attribute values. Extensive experiments on various real and synthetic networks show that our late-fusion approach can improve detection accuracy from using only network structure. Moreover, our approach runs significantly faster than other attributed community detection algorithms including early fusion ones. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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