DocumentCode :
1757989
Title :
Sequential Changepoint Approach for Online Community Detection
Author :
Marangoni-Simonsen, David ; Yao Xie
Author_Institution :
H. Milton Stewart Sch. of Ind. & Syst. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Volume :
22
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
1035
Lastpage :
1039
Abstract :
We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdös-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop three algorithms: the Exhaustive Search (ES), the Mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these methods is evaluated by the average run length (ARL), which captures the frequency of false alarms, and the detection delay. Numerical comparisons show that the ES method performs the best; however, it is exponentially complex. The Mixture method is polynomially complex by exploiting the fact that the size of the community is typically small in a large network. However, it may react to a group of active edges that do not form a community. This issue is resolved by the H-Mix method, which is based on a dendrogram decomposition of the network. We present an asymptotic analytical expression for ARL of the Mixture method when the threshold is large.
Keywords :
computational complexity; graph theory; mixture models; probability; search problems; social networking (online); ARL; ES method; Erdös-Renyi random graphs; H-mix methods; asymptotic analytical expression; average run length; community size; dendrogram decomposition; detection delay; exhaustive search; false alarms frequency; hierarchical mixture methods; online community detection; polynomial complexity; probability; social network; statistical changepoint detection methodology; Communities; Delays; Electronic mail; Image edge detection; Signal processing algorithms; Social network services; Testing; Changepoint detection; community detection; sequential methods; social networks;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2014.2381553
Filename :
6985719
Link To Document :
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