Paper Info
Title
Detect structural-connected communities based on BSCHEF in C-DBLP.
Abstract
Chinese Digital Bibliography & Library Project C-DBLP is a huge and real-life co-author social network in China, rarely cited by published paper. It contains a large amount of ground-truth community structure with distinguished research topics. Despite the fact that rich studies on community detection have been conducted with gains of practically fruitful algorithms, unfortunately, with the coming of 'Big Data' era and speedy development of mobile devices, social networks like C-DBLP have incredibly expanded on nodes and edges, as a result, because of massive data cardinality, a large portion of community detection methods consume memory resource excessively. Therefore, in this work, we select Based on Structural Connection Hierarchical Exploration BSCHE algorithm to partition nodes in C-DBLP because of its On time cost, fast enough to process massive data, and its novel physical meaning of similarity between nodes defined by structural connection and availability. In addition, in order to avoid huge memory resource consumption caused by 'Big Data' of C-DBLP, we strengthen BSCHE as a framework BSCHEF by our proposed 'count-pointer-strategy' imitated from incremental batch process to detect co-author communities on C-DBLP. The experiment results show that BSCHEF can find sets of communities on C-DBLP more effectively with the highest modularity value and the least execution time compared to other clustering algorithm. Copyright © 2015 John Wiley & Sons, Ltd.
Year
DOI
Venue
2016
10.1002/cpe.3437
Concurrency and Computation: Practice and Experience
Keywords
DocType
Volume
community detection,structural connection,scalability,C-DBLP
Journal
28
Issue
ISSN
Citations 
2
1532-0626
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Tinghuai Ma131440.76
Huan Rong200.34
Changhong Ying300.34
Yuan Tian427021.90
Abdullah Al-Dhelaan552339.77
Mznah Al-Rodhaan630622.90