Detect structural‐connected communities based on BSCHEF in C‐DBLP
Detect structural‐connected communities based on BSCHEF in C‐DBLP
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DOI：
10.1002/cpe.3437
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Tinghuai Ma;Huan Rong;Changhong Ying;Yuan Tian;A. Al-Dhelaan;Mznah Al-Rodhaan
中科院分区：
文献类型：
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作者：
Tinghuai Ma;Huan Rong;Changhong Ying;Yuan Tian;A. Al-Dhelaan;Mznah Al-Rodhaan
中国数字书目与图书馆项目（C-DBLP）是中国一个庞大的、真实的生活中的合著者社交网络，很少被已发表的论文引用。它包含了大量的地面真理社区结构与杰出的研究课题。尽管人们已经对社区检测进行了大量的研究，并获得了实际上富有成效的算法，但不幸的是，随着“大数据”时代的到来和移动的设备的快速发展，像C‐DBLP这样的社交网络在节点和边缘上进行了难以置信的扩展，因此，由于大量的数据基数，很大一部分社区检测方法过度消耗内存资源。因此，在这项工作中，我们选择基于结构连接层次探索（BSCHE）算法来划分C-DBLP中的节点，因为它的时间开销为O（n），处理海量数据的速度足够快，并且它由结构连接和可用性定义的节点之间的相似性具有新颖的物理意义。此外，为了避免C-DBLP的“大数据”造成的巨大内存资源消耗，我们通过模仿增量批处理过程提出的“计数指针策略”来加强BSCHE作为一个框架（BSCHEF），以检测C-DBLP上的合著者社区。实验结果表明，与其他聚类算法相比，BSCHEF算法能够更有效地发现C-DBLP上的社区集合，具有最高的模块度值和最少的执行时间。版权所有© 2015约翰威利父子有限公司.
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 ‘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.