Active link selection for efficient semi-supervised community detection
نویسندگان
چکیده
منابع مشابه
Active link selection for efficient semi-supervised community detection
Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised informati...
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Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, ...
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Community detection is the fundamental problem in the analysis and understanding of complex networks, which has attracted a lot of attention in the last decade. Active learning aims to achieve high accuracy using as few labeled data as possible. However, so far as we know, active learning has not been applied to detect community to improve the performance of discovering community structure of c...
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Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtai...
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Community detection in real-world graphs has been shown to benefit from using multi-aspect information, e.g., in the form of “means of communication” between nodes in the network. An orthogonal line of work, broadly construed as semi-supervised learning, approaches the problem by introducing a small percentage of node assignments to communities and propagates that knowledge throughout the graph...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2015
ISSN: 2045-2322
DOI: 10.1038/srep09039