Tolerance Granulation Based Community Detection Algorithm

作     者：Shu Zhao Wang Ke Jie Chen Feng Liu Menghan Huang Yanping Zhang Jie Tang 

作者机构：Key Laboratory of Intelligent Computing and Signal Processing of Ministry of EducationCenter of Information Support & Assurance TechnologySchool of Computer Science and TechnologyAnhui University School of Computer Science and TechnologyTsinghua University 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2015年第20卷第6期

页      面：620-626页

核心收录：

学科分类：07[理学] 08[工学] 070104[理学-应用数学] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：partially supported by the National HighTech Research and Development (863) Program of China (No. 2015AA124102) the National Natural Science Foundation of China (Nos. 61402006 and 61175046) the Provincial Natural Science Research Program of Higher Education Institutions of Anhui Province (No. KJ2013A016) the Provincial Natural Science Foundation of Anhui Province (No. 1508085MF113) the College Students National Innovation & Entrepreneurship Training program of Anhui University (No. 201410357041) the Recruitment Project of Anhui University for Academic and Technology Leader 

主　　题：tolerance relation community tolerance granulation 

摘      要：Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual Information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.