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Exploring Triangle-Free Dense Structures

delete2018-02-01
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PRE
AI
C
Can Lu
J
Jeffrey Xu Yu *
W
Wei, Hao
DOI:10.1109/TKDE.2017.2764468delete
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摘要

摘要

En 中文
Triadic closure is ubiquitous in social networks, which refers to the property among three individuals, A, B, and C, such that if there exist strong ties between A-B and A-C, then there must be a strong or weak tie between B-C. Related to triadic closure, the number of triangles has been extensively studied since it can be effectively used as a metric to analyze the structure and function of a network. In this paper, from a different viewpoint, we study triangle-free dense structures which have received little attention. We focus on K-3,K-3 where there are two subsets of three vertices, a vertex in a subset has an edge connected to every vertex in another subset while it does not have an edge to any other vertex in the same subset. Such K-n,K-n in general implies a philosophy contradiction: (a) Any two individuals are friends if they have no common friends, and (b) Any two individuals are not friends if they have common friends. However, we find such induced K-3,K-3 does exist frequently, and they do not disappear over time over a real academic collaboration network. In addition, in the real datasets tested, nearly all edges appearing in K-3,K-3 appear in some triangles. We analyze the expected numbers of induced K-3,K-3 and triangles (Delta) in four representative random graph models, namely, Erdos-Renyi random graph model, Watts-Strogatz small-world model, Barabasi-Albert preferential attachment model, and configuration model, and give an algorithm to enumerate all distinct K-3,K-3 in an undirected social network. We conduct extensive experiments on both real and synthetic datasets to confirm our findings. As an application, such K-3,K-3 found helps to find new stars collaborated by well-known figures who themselves do not collaborate.
Keyword:
Triangle-free
dense structures
social networks
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期刊

IEEE Transactions on Knowledge and Data Engineering 封面图
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
论文数:
6.9K
被引数:
3.2W

机构

C
Chinese University of Hong Kong
学者数:
3.4W
论文数: 3.2W
被引数: 5.6W
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