LinkBlackHole*: Robust Overlapping Community Detection Using Link Embedding

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This paper proposes LinkBlackHole*, a novel algorithm for finding communities that are (i) overlapping in nodes and (ii) mixing (not separating clearly) in links. There has been a small body of work in each category, but this paper is the first one that addresses both. LinkBlackHole* is a merger of our earlier two algorithms, LinkSCAN* and BlackHole, inheriting their advantages in support of highly-mixed overlapping communities. The former is used to handle overlapping nodes, and the latter to handle mixing links in finding communities. Like LinkSCAN and its more efficient variant LinkSCAN*, this paper presents LinkBlackHole and its more efficient variant LinkBlackHole*, which reduces the number of links through random sampling. Thorough experiments show superior quality of the communities detected by LinkBlackHole* and LinkBlackHole to those detected by other state-of-the-art algorithms. In addition, LinkBlackHole* shows high resilience to the link sampling effect, and its running time scales up almost linearly with the number of links in a network.
Publisher
IEEE COMPUTER SOC
Issue Date
2019-11
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.31, no.11, pp.2138 - 2150

ISSN
1041-4347
DOI
10.1109/TKDE.2018.2873750
URI
http://hdl.handle.net/10203/269780
Appears in Collection
IE-Journal Papers(저널논문)
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