NDLI: Discovering Social Circles in Directed Graphs

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Discovering Social Circles in Directed Graphs

Content Provider	ACM Digital Library
Author	Giraud-carrier, Christophe G. Burton, Scott H.
Copyright Year	2014
Abstract	We examine the problem of identifying social circles, or sets of cohesive and mutually aware nodes surrounding an initial query set, in directed graphs where the complete graph is not known beforehand. This problem differs from local community mining, in that the query set defines the circle of interest. We explicitly handle edge direction, as in many cases relationships are not symmetric, and focus on the local context because many real-world graphs cannot be feasibly known. We outline several issues that are unique to this context, introduce a quality function to measure the value of including a particular node in an emerging social circle, and describe a greedy social circle discovery algorithm. We demonstrate the effectiveness of this approach on artificial benchmarks, large networks with topical community labels, and several real-world case studies.
Starting Page	1
Ending Page	27
Page Count	27
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2641759
Volume Number	8
Issue Number	4
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Social circles Directed graphs Local community search
Content Type	Text
Resource Type	Article
Subject	Computer Science

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