LabelRankT: Incremental Community Detection in Dynamic Networks via
Label Propagation
release_rwgbdltpsnc47oecidaghohn3i
by
Jierui Xie, Mingming Chen, Boleslaw K. Szymanski
2013
Abstract
An increasingly important challenge in network analysis is efficient
detection and tracking of communities in dynamic networks for which changes
arrive as a stream. There is a need for algorithms that can incrementally
update and monitor communities whose evolution generates huge realtime data
streams, such as the Internet or on-line social networks. In this paper, we
propose LabelRankT, an online distributed algorithm for detection of
communities in large-scale dynamic networks through stabilized label
propagation. Results of tests on real-world networks demonstrate that
LabelRankT has much lower computational costs than other algorithms. It also
improves the quality of the detected communities compared to dynamic detection
methods and matches the quality achieved by static detection approaches. Unlike
most of other algorithms which apply only to binary networks, LabelRankT works
on weighted and directed networks, which provides a flexible and promising
solution for real-world applications.
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