LabelRankT: Incremental Community Detection in Dynamic Networks via Label Propagation release_rwgbdltpsnc47oecidaghohn3i

by Jierui Xie, Mingming Chen, Boleslaw K. Szymanski

Released as a paper-conference .

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.
In text/plain format

Archived Files and Locations

application/pdf  444.5 kB
file_n2z6ecg27racndhdt2gmceuhw4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  paper-conference
Stage   submitted
Date   2013-05-12
Version   v2
Language   en ?
arXiv  1305.2006v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 4b69e369-f565-47ff-bd44-f8139fb177b9
API URL: JSON