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Active semi-supervised overlapping community finding with pairwise constraints
Author(s)
Date Issued
2019-08-23
Date Available
2019-08-27T07:12:10Z
Abstract
Algorithms for finding communities in complex networks are generally unsupervised, relying solely on the structure of the network. However, these methods can often fail to uncover meaningful groupings that reflect the underlying communities in the data, particularly when they are highly overlapping. One way to improve these algorithms is by incorporating human expertise or background knowledge in the form of pairwise constraints to direct the community detection process. In this work, we explore the potential of semi-supervised strategies to improve algorithms for finding overlapping communities in networks. We propose a method, based on label propagation, for finding communities using pairwise constraints. Furthermore, we introduce a new strategy, inspired by active learning, for intelligent constraint selection, which is designed to minimize the level of human annotation required. Extensive evaluations on synthetic and real-world datasets demonstrate the potential of this strategy for effectively uncovering meaningful overlapping community structures, using a limited amount of supervision.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
The Ministry of Higher Education in Saudi Arabia
Type of Material
Journal Article
Publisher
Springer
Journal
Applied Network Science
Volume
4
Issue
63
Start Page
1
End Page
27
Copyright (Published Version)
2019 the Authors
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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