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Sensors 2019, 19(2), 260; https://doi.org/10.3390/s19020260

NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

School of Software, Tsinghua University, Beijing 100084, China
This paper is an extended version of our paper published in Huang, B.; Wang, C.; Qian, J. DoOC: A Platform for Domain-oriented Online Community System Construction. In Proceedings of the Smart Data 2018, Halifax, Canada, 30 July–3 August 2018.
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Received: 30 November 2018 / Revised: 4 January 2019 / Accepted: 6 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue Selected papers from Smart Data 2018 & Big Data Service 2018)
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Abstract

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods. View Full-Text
Keywords: overlapping community detection; attributed networks; multi-label propagation; node similarity overlapping community detection; attributed networks; multi-label propagation; node similarity
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Huang, B.; Wang, C.; Wang, B. NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach. Sensors 2019, 19, 260.

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