IncOrder: Incremental density-based community detection in dynamic networks

Knowledge-Based Systems

Volumn 72, Issue , 2014, Pages 1-12

  Sun, Heli ^a,d   Huang, Jianbin ^b,d   Zhang, Xin ^b   Liu, Jiao ^a   Wang, Dong ^c   Liu, Huailiang ^b   Zou, Jianhua ^a   Song, Qinbao ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b XIDIAN UNIVERSITY   (China)

^c NORTHWEST UNIVERSITY   (China)

^d NANJING UNIVERSITY   (China)

Author keywords

Community detection; Density based clustering; Dynamic network; Incremental method; Modularity optimazition

Indexed keywords

POPULATION DYNAMICS;

COMMUNITY DETECTION; DENSITY-BASED CLUSTERING; DYNAMIC NETWORK; INCREMENTAL METHOD; MODULARITY OPTIMAZITION;

CLUSTERING ALGORITHMS;

EID: 84909964702     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.07.015     Document Type: Article

References (33)

1. Michelle Girvan, and Mark E.J. Newman Community structure in social and biological networks Proc. Nat. Acad. Sci. 99 12 2002 7821 7826
2. Zhongying Zhao, Shengzhong Feng, Qiang Wang, Joshua Zhexue Huang, Graham J. Williams, and Jianping Fan Topic oriented community detection through social objects and link analysis in social networks Knowl.-Based Syst. 26 2012 164 173
3. Babak Amiri, Liaquat Hossain, John W. Crawford, and Rolf T. Wigand Community detection in complex networks: multi-objective enhanced firefly algorithm Knowl.-Based Syst. 46 2013 1 11
4. Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, and Thomas A.J. Schweiger Scan: a structural clustering algorithm for networks Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2007 ACM 824 833
5. Heli Sun, Jianbin Huang, Jiawei Han, Hongbo Deng, Peixiang Zhao, and Boqin Feng gskeletonclu: Density-based network clustering via structure-connected tree division or agglomeration 2010 IEEE 10th International Conference on Data Mining (ICDM) 2010 IEEE 481 490
6. J. Huang, H. Sun, D. Bortner, and Y. Liu Mining hierarchical community structure within networks from density-connected traveling orders J. Softw. 22 5 2011 951 961 (in Chinese)
7. Martin Ester, Hans-Peter Kriegel, Jörg Sander, Michael Wimmer, Xiaowei Xu, Incremental clustering for mining in a data warehousing environment, in: VLDB, vol. 98, 1998, pp. 323-333.
8. Hans-Peter Kriegel, Peer Kröoger, and Irina Gotlibovich Incremental optics: efficient computation of updates in a hierarchical cluster ordering Data Warehousing and Knowledge Discovery 2003 Springer 224 233
9. Brian W. Kernighan, and Shen Lin An efficient heuristic procedure for partitioning graphs Bell Syst. Tech. J. 49 2 1970 291 307
10. Gary William Flake, Steve Lawrence, and C. Lee Giles Efficient identification of web communities Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2000 ACM 150 160
11. Scott White, and Padhraic Smyth A spectral clustering approach to finding communities in graph SDM vol. 5 2005 SIAM 76 84
12. Aaron Clauset, Mark E.J. Newman, and Cristopher Moore Finding community structure in very large networks Phys. Rev. E 70 6 2004 066111
13. Stijn Marinus van Dongen, Graph Clustering by Flow Simulation, 2000.
14. Usha Nandini Raghavan, Réka Albert, and Soundar Kumara Near linear time algorithm to detect community structures in large-scale networks Phys. Rev. E 76 3 2007 036106
15. Martin Rosvall, and Carl T. Bergstrom Maps of random walks on complex networks reveal community structure Proc. Nat. Acad. Sci. 105 4 2008 1118 1123
16. Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: KDD, vol. 96, 1996, pp. 226-231.
17. Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander Optics: ordering points to identify the clustering structure ACM SIGMOD Rec. vol. 28 1999 ACM 49 60
18. Jianbin Huang, Heli Sun, Jiawei Han, Hongbo Deng, Yizhou Sun, and Yaguang Liu Shrink: a structural clustering algorithm for detecting hierarchical communities in networks Proceedings of the 19th ACM International Conference on Information and Knowledge Management 2010 ACM 219 228
19. Tanja Falkowski, Anja Barth, and Myra Spiliopoulou Dengraph: a density-based community detection algorithm IEEE/WIC/ACM International Conference on Web Intelligence 2007 IEEE 112 115
20. Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou, and Philip S. Yu Graphscope: parameter-free mining of large time-evolving graphs Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2007 ACM 687 696
21. Charu C. Aggarwal, Yuchen Zhao, and S. Yu Philip On clustering graph streams SDM 2010 SIAM 478 489
22. Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L. Tseng Facetnet: a framework for analyzing communities and their evolutions in dynamic networks Proceedings of the 17th International Conference on World Wide Web 2008 ACM 685 694
23. Huazhong Ning, Wei Xu, Yun Chi, Yihong Gong, Thomas S Huang, Incremental spectral clustering with application to monitoring of evolving blog communities, in: Proceedings of the SIAM International Conference on Data Mining (SDM'07), 2007, pp. 261-272.
24. Mark E.J. Newman Modularity and community structure in networks Proc. Nat. Acad. Sci. 103 23 2006 8577 8582
25. Zhidan Feng, Xiaowei Xu, Nurcan Yuruk, and Thomas A.J. Schweiger A novel similarity-based modularity function for graph partitioning Data Warehousing and Knowledge Discovery 2007 Springer 385 396
26. David L. Davies, and Donald W. Bouldin A cluster separation measure IEEE Trans. Pattern Anal. Mach. Intell. 2 1979 224 227
27. Kaufman Leonard, J.R. Peter, Finding Groups in Data: An Introduction to Cluster Analysis, 1990.
28. http://www-personal.umich.edu/mejn/netdata/.
29. http://snap.stanford.edu/data/.
30. Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi Benchmark graphs for testing community detection algorithms Phys. Rev. E 78 4 2008 046110
31. Nguyen Xuan Vinh, Julien Epps, and James Bailey Information theoretic measures for clusterings comparison: is a correction for chance necessary? Proceedings of the 26th Annual International Conference on Machine Learning 2009 ACM 1073 1080
32. Lawrence Hubert, and Phipps Arabie Comparing partitions J. Classif. 2 1 1985 193 218
33. William M. Rand Objective criteria for the evaluation of clustering methods J. Am. Stat. Assoc. 66 336 1971 846 850