The research on detecting complex network community structure

Wang, Zongjiang

doi:10.1109/iccrd.2011.5764270

Cited by 1 publication

(3 citation statements)

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“…Firstly, even though those pseudo-communities have been detected in [16], outlier and intermediary points (the point with high betweenness value)in network are still ignored whose affiliation will affect the accuracy and precision of community detection. Firstly, even though those pseudo-communities have been detected in [16], outlier and intermediary points (the point with high betweenness value)in network are still ignored whose affiliation will affect the accuracy and precision of community detection.…”

Section: Related Workmentioning

confidence: 99%

“…Qing Cheng et al [18] proposed a kind of hierarchical clustering method based on Hyper-edge similarity to simultaneously detect both the overlapping and hierarchical properties of complex community structure, as well as using the newly introduced community density to evaluate the goodness of a community.Unfortunately, compared to BSCHE algorithm, there still exists some underlying defects of algorithms or methods adopted in aforementioned works. Firstly, even though those pseudo-communities have been detected in [16], outlier and intermediary points (the point with high betweenness value)in network are still ignored whose affiliation will affect the accuracy and precision of community detection. Secondly, the time complexity of aforementioned algorithms or methods cannot be limited within a linear increase, which is especially worse in [10,11,14,17].…”

mentioning

confidence: 99%

“…Unfortunately, compared to BSCHE algorithm, there still exists some underlying defects of algorithms or methods adopted in aforementioned works. Firstly, even though those pseudo-communities have been detected in [16], outlier and intermediary points (the point with high betweenness value)in network are still ignored whose affiliation will affect the accuracy and precision of community detection. Secondly, the time complexity of aforementioned algorithms or methods cannot be limited within a linear increase, which is especially worse in [10,11,14,17].…”

mentioning

confidence: 99%

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Detect structural‐connected communities based on BSCHEF in C‐DBLP

Rong

Ying³

et al. 2015

Concurrency and Computation

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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 O(n) 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 'countpointer-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.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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