[1] MA T, WANG Y, TANG M, et al. LED:a fast overlapping communities detection algorithm based on structural clustering[J].Neurocomputing, 2016, 207:488-500. [2] 时京晶. 三种经典复杂网络社区结构划分算法研究[J]. 电脑与信息技术, 2011,19(4):41-43.(SHI J J. Research on three classical complex network community structure partition algorithms[J]. Computer and Information Technology, 2011, 19(4):41-43.) [3] 赵建军,汪清,由磊,等. 基于信息传递和峰值聚类的自适应社区发现算法[J]. 重庆大学学报, 2018, 41(11):76-83. (ZHAO J J, WANG Q, YOU L, et al. Adaptive community discovery algorithm based on information transfer and peak clustering[J].Journal of Chongqing University, 2018, 41(11):76-83.) [4] 刘大有,金弟,何东晓, 等.复杂网络社区挖掘综述[J]. 计算机研究与发展, 2013, 50(10):2140-2154. (LIU D Y, JIN D, HE D X, et al. Review of mining of complex network communities[J].Journal of Computer Research and Development, 2013, 50(10):2140-2154.) [5] 朱牧,孟凡荣,周勇. 基于链接密度聚类的重叠社区发现算法[J]. 计算机研究与发展, 2013, 50(12):2520-2530. (ZHU M, MENG F R, ZHOU Y. Overlapping community detection algorithm based on link density clustering[J]. Journal of Computer Research and Development, 2013, 50(12):2520-2530.) [6] SARSWAT A, GUDDETI R M R. A novel overlapping community detection using parallel CFM and sequential nash equilibrium[C]//Proceedings of the 2018 10th International Conference on Communication Systems & Networks. Piscataway:IEEE, 2018:649-654. [7] PALLA G, DERÉNYI I, FARKAS I, et al. Uncovering the overlapping community structure of complex networks in nature and society[J]. Nature, 2005, 435(7043):814-818. [8] FARKAS I, ÁBEL D, PALLA G, et al. Weighted network modules[J]. New Journal of Physics, 2007, 9:180. [9] LANCICHINETTI A, FORTUNATO S, KERTESZ J. Detecting the overlapping and hierarchical community structure in complex networks[J]. New Journal of Physics, 2009, 11:033015. [10] ZHANG S, WANG R S, ZHANG X S. Identification of overlapping community structure in complex networks using fuzzy c-means clustering[J]. Physica A:Statistical Mechanics and its Applications, 2007, 374(1):483-490. [11] AHN Y Y, BAGROW J P, LEHMANN S. Link communities reveal multiscale complexity in networks[J]. Nature, 2010, 466(7307):761. [12] GREGORY S. An algorithm to find overlapping community structure in networks[C]//Proceedings of the 11th European Conference on Principles of Data Mining and Knowledge Discovery. Berlin:Springer, 2007:91-102. [13] RAGHAVAN U N, ALBERT R, KUMARA S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical Review E, 2007, 76(3):036106. [14] 张振宇,朱培栋,王可,等.拓扑结构与节点属性综合分析的社区发现算法[J].计算机技术与发展,2018,28(4):1-5.(ZHANG Z Y, ZHU P D, WANG K, et al. Community detection algorithm for comprehensive analysis of topology and node attributes[J]. Computer Technology and Development,2018,28(4):1-5.) [15] TIAN Y, HANKINS R A, PATEL J M. Efficient aggregation for graph summarization[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2008:567-580. [16] FREY B J, DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814):972-976. [17] WANG M, ZUO W, WANG Y. An improved density peaks-based clustering method for social circle discovery in social networks[J]. Neurocomputing, 2016, 179:219-227. [18] HE T, CHAN K C C. MISAGA:an algorithm for mining interesting subgraphs in attributed graphs[J]. IEEE Transactions on Cybernetics, 2017, 48(5):1369-1382. [19] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J].Science,2014, 344(6191):1492-1496. [20] BENSON A R, GLEICH D F, LESKOVEC J. Higher-order organization of complex networks[J].Science, 2016,353(6295):163-166. [21] LI P Z, HUANG L, WANG C D, et al. Community detection using attribute homogenous motif[J]. IEEE Access, 2018, 6:47707-47716. [22] CHEN X, XIA C, WANG J. A novel trust-based community detection algorithm used in social networks[J]. Chaos, Solitons & Fractals, 2018, 108:57-65. [23] LESKOVEC J,KREVL A. Stanford large network dataset collection[DB/OL].[2019-03-02].https://snap.standford.edu/data/index.html. [24] SHEN H, CHENG X, CAI K, et al. Detect overlapping and hierarchical community structure in networks[J]. Physica A:Statistical Mechanics and Its Applications, 2009, 388(8):1706-1712. [25] BU Z, GAO G, WU Z, et al. Local community extraction for non-overlapping and overlapping community detection[C]//Proceedings of the 10th International Conference on Advanced Data Mining and Applications. Berlin:Springer, 2014:98-111. |