ArticleScientific reports2013
Enhanced community structure detection in complex networks with partial background information.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- mAging · 2022Article
- Overlapping community finding with noisy pairwise constraints.Applied network science · 2020Article
- Overlapping functional modules detection in PPI network with pair-wise constrained non-negative matrix tri-factorisation.IET systems biology · 2018Article
- Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.Scientific reports · 2017Article
- Article
- Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model.PloS one · 2017Article
- Structure and inference in annotated networks.Nature communications · 2016Article
- Active link selection for efficient semi-supervised community detection.Scientific reports · 2015Article
- Enhanced community structure detection in complex networks with partial background information.Scientific reports · 2013Article
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Authors and funding
3 authors.
Funding
Abstract
Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework.
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Registered trials
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