Abstract
Community detection can not only help people understand organizational structure and function of complex networks, but also attributes to many potential applications including targeted advertising and customer relationship management. Due to the low time complexity, the label propagation algorithm is widely used, but there is still room to improve the community quality and the detection stability. Inspired by resource allocation and local path similarity, we first give a new two-level neighbourhood similarity measure called TNS, and on this basis we propose an improved label propagation algorithm for community detection. In this new algorithm, the minimum distance and local centrality index are considered to select the initial community centers, to ensure that they are both important and far away from each other. In the process of forming initial community, we employ the new similarity measure and an optimization strategy of asynchronously updating labels according to node importance. To further improve the accuracy of community division, we introduce the label influence based on the new similarity measure to further optimize the community division of networks. The experimental results on both the artificial network and ten real-world networks show that our proposed algorithm has better comprehensive performance than several existing algorithms in terms of modularity, normalized mutual information and adjusted rand index.
Highlights
Since Newman’s original work [1], during the past two decades, community detection in complex networks has attracted considerable attention [2]–[8]
We present an improved label propagation algorithm for community detection based on two-level neighbourhood similarity measure
PROPOSED ALGORITHM In this article, we propose an improved label propagation algorithm called two-level neighborhood similarity (TNS)-LPA, which is consisted of three phases
Summary
Since Newman’s original work [1], during the past two decades, community detection in complex networks has attracted considerable attention [2]–[8]. Mining the community structure in social networks can help us analyze the network topology and function, so as to understand, control and predict social networks. Most of social networks have obvious community structures. Community detection based on social media data can be employed in various applications, including epidemic control, crisis response, and predictive policing [9]–[13]. In Internet finance, community detection can be used in targeted advertising, customer relationship management and fraud detection [14], [15].
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