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 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 (c) 2015 John Wiley & Sons, Ltd.