Community detection has attracted great interest in graph analysis and mining during the past decade, and a great number of approaches have been developed to address this problem. However, the lack of a uniform framework and a reasonable evaluation method makes it a puzzle to analyze, compare and evaluate the extensive work, let alone picking out a best one when necessary. In this paper, we design a tool called CoDAR, which reveals the generalized procedure of community detection and monitors the real-time structural changes of network during the detection process. Moreover, CoDAR adopts 12 recognized metrics and builds a rating model for performance evaluation of communities to recom- mend the best-performing algorithm. Finally, the tool also provides nice interactive windows for display.
['Xiang Ying', 'Chaokun Wang', 'Meng Wang', 'Jeffrey Xu Yu', 'Jun Zhang']
CoDAR: Revealing the Generalized Procedure & Recommending Algorithms of Community Detection