NDLI: Toward seed-insensitive solutions to local community detection

Content Provider	Springer Nature Link
Author	Ma, Lianhang Huang, Hao He, Qinming Chiew, Kevin Liu, Zhenguang
Copyright Year	2014
Abstract	Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.
Starting Page	183
Ending Page	203
Page Count	21
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	43
Issue Number	1
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2014-04-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Local community detection Similarity Seed-insensitivity Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Business Information Systems Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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