Link Similarity Reveals Multiscale Link Communities

2015 ◽  
pp. 111-116
Author(s):  
Guishen Wang ◽  
Lan Huang
Keyword(s):  
Link Similarity

Local Community Detection Using Link Similarity

2012 ◽  
Vol 27 (6) ◽  
pp. 1261-1268 ◽  
Author(s):  
Ying-Jun Wu ◽  
Han Huang ◽  
Zhi-Feng Hao ◽  
Feng Chen
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Local Community Detection ◽  
Link Similarity

Using a Joint Link Similarity Evaluation Based Method for Crawling the Resources on Web

2011 ◽  
Vol 33 (12) ◽  
pp. 2267-2280
Author(s):  
Nai-Zhou ZHANG ◽  
Shi-Jun LI ◽  
Wei YU ◽  
Zhuo ZHANG
Keyword(s):  
Link Similarity

scholarly journals Link Clustering with Extended Link Similarity and EQ Evaluation Division

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e66005 ◽  
Author(s):  
Lan Huang ◽  
Guishen Wang ◽  
Yan Wang ◽  
Enrico Blanzieri ◽  
Chao Su
Keyword(s):  
Link Similarity

Link community detection based on line graphs with a novel link similarity measure

2016 ◽  
Vol 30 (06) ◽  
pp. 1650023 ◽  
Author(s):  
Guishen Wang ◽  
Lan Huang ◽  
Yan Wang ◽  
Wei Pang ◽  
Qin Ma
Keyword(s):  
Community Detection ◽  
Similarity Measure ◽  
Detection Algorithm ◽  
Line Graphs ◽  
The Novel ◽  
On Line ◽  
Community Detection Algorithm ◽  
Relationship Of ◽  
The Relationship ◽  
Link Similarity

Link community gradually unfolds its capacity in complex network research. In this paper, a novel link similarity measure on line graphs is proposed. This measure can be adapted to different types of networks with an adjustable parameter. We prove its value converges to a limit on line graphs with the relationship of the nonneighbor links taken into account. Based on this similarity measure, we propose a novel link community detection algorithm for link clustering on line graphs. The detection algorithm combines the novel link similarity measure with the classic Markov Cluster (MCL) Algorithm and determines the link community partitions by calculating an extended modularity measure. Extensive experiments on two types of complex networks demonstrate the effectiveness, reliability and rationality of our solution in contrast to the other two classical algorithms.

Weight-Discounted Symmetrization in Clustering Directed Graphs

2013 ◽  
Vol 756-759 ◽  
pp. 2979-2987 ◽  
Author(s):  
Bin He ◽  
Hui Liu ◽  
Xiang Hui Zhao ◽  
Ze Feng Li
Keyword(s):  
Directed Graph ◽  
Real World ◽  
State Of The Art ◽  
Directed Graphs ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Citation Networks ◽  
Second Stage ◽  
Node Ranking ◽  
Link Similarity

An increasing attention has been recently devoted to uncovering community structure in directed graphs which widely exist in real-world complex networks such as social networks, citation networks, World Wide Web, email networks, etc. A two-stage framework for detecting clusters is an effective way for clustering directed graphs while the first stage is to symmetrize the directed graph using some similarity measures. Any state-of-the-art clustering algorithms for undirected graphs can be leveraged in the second stage. Hence, both stages are important to the effectiveness of the clustering result. However, existing symmetrization methods only consider about the direction of edges but ignore the weights of nodes. In this paper, we first attempt to connect link analysis in directed graph clustering. This connection not only takes into consideration the directionality of edges but also uses node ranking scores such as authority and hub score to explicitly capture in-link and out-link similarity. We also demonstrate the generality of our proposed method by showing that existing state-of-the-art symmetrization methods can be derived from our method. Empirical validation shows that our method can find communities effectively in real world networks.

Module overlapping structure detection in PPI using an improved link similarity-based Markov clustering algorithm

2018 ◽  
Vol 31 (5) ◽  
pp. 1481-1490 ◽  
Author(s):  
L. Gu ◽  
Y. Han ◽  
C. Wang ◽  
Wei Chen ◽  
Jun Jiao ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Structure Detection ◽  
Markov Clustering ◽  
Link Similarity
Sign in / Sign up

Export Citation Format

Share Document