Correlation in mobile call networks from structure
perspective
Correlation in mobile call networks from structure
perspective
Bin WU , Deyong HU , Qi YE , Bai WANG ,
Beijing Key Laboratory
of Intelligent Telecommunications Software and Multimedia, Beijing
University of Posts and Telecommunications, Beijing 100876, China;
Abstract:Researchers have done considerable work on the structure of social network recently, but mostly neglected the correlation between two connected nodes. In this paper, our primary goal is to acquire users’ structural properties in mobile call networks. We take a novel perspective—structure correlation between two connected users perspective to study the structural properties. To investigate the structural properties in static and dynamic mobile call networks, we define some metrics which are based on the clique size vectors of mobile call users. By exploring several realworldmobile call networks, which contain hundreds of thousands of mobile call users respectively, we find that people tend to communicate with the one who has a similar structure in static mobile call networks. Moreover, It is found that the connected people have similar structural changes on the whole in dynamicmobile call networks, and the structures of some two connected persons both have growing or shrinking trends. We use a visualization toolkit to give a view of the growing or shrinking scenarios temporally.
. Correlation in mobile call networks from structure
perspective[J]. Front. Comput. Sci., 2009, 3(3): 347-355.
Bin WU , Deyong HU , Qi YE , Bai WANG , . Correlation in mobile call networks from structure
perspective. Front. Comput. Sci., 2009, 3(3): 347-355.
Singla P, Richardson M. Yes, there is a correlation:- from social networks to personal behavior on the web. In: Proceedings of the 17th International Conference on World WideWeb, 2008, 655―664
Leskovec J, Horvitz E. Planetary-scale views ona large instantmessaging network. In: Proceedingsof the 17th International Conference on World Wide Web, 2008, 915―924
Nanavati A A, Singh R, Chakraborty D, et al. Analyzing the structure and evolution of massivetelecom graphs. IEEE Transactions on Knowledgeand Data Engineering, 2008, 20(5): 703―718 doi: 10.1109/TKDE.2007.190733
Gonzalez M C, Hidalgo C A, Barabási A L. Understanding individual human mobilitypatterns. Nature, 2008, 453(7196): 779―782 doi: 10.1038/nature06958
Ye Q, Wu B, Wang B. Jsnva: A java straightline drawing framework for networkvisual analysis. In: Proceedings of the4th International Conference on Advanced Data Mining and Applications, 2008, 667―674
Fisher D. Usingegocentric networks to understand communication. IEEE Internet Computing, 2005, 9(5): 20―28 doi: 10.1109/MIC.2005.114
Watts D J, Strogatz S H. Collective dynamics of ‘small-world’networks. Nature, 1998, 393(6684): 440―442 doi: 10.1038/30918
Seshadri M, Machiraju S, Sridharan A, et al. Mobile call graphs: beyond power-law and lognormaldistributions. In: Proceeding of the 14thACM SIGKDD International Conference on Knowledge Discovery and DataMining, 2008, 596―604
Jin E M, Girvan M, Newman M E J. Structure of growing social networks. Physical Review E, 2001, 64(4): 046132 doi: 10.1103/PhysRevE.64.046132
Leskovec J, Backstrom L, Kumar R, et al. Microscopic evolution of social networks. In: Proceeding of the 14th ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining, 2008, 462―470
Dasgupta K, Singh R, Viswanathan B, et al. Social ties and their relevance to churn inmobile telecom networks. In: Proceedingsof the 11th International Conference on Extending database technology, 2008, 668―677
Onnela J P, Saramaki J, Hyvonen J, et al. Structure and tie strengths in mobile communicationnetworks. In: Proceedings of the NationalAcademy of Sciences, 2007, 104(18): 7332―7336 doi: 10.1073/pnas.0610245104
Nanavati A A, Gurumurthy S, Das G, et al. On the structural properties of massive telecomcall graphs: findings and implications. In: Proceedings of the 15th ACM International Conference on Informationand Knowledge Management, 2006, 435―444
Mcpherson M, Lovin L S, Cook JM. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 2001, 27(1): 415―444 doi: 10.1146/annurev.soc.27.1.415
Anagnostopoulos A, Kumar R, Mahdian M. Influence and correlation in social networks. In: Proceeding of the 14th ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining, 2008, 7―15
Asur S, Parthasarathy S, Ucar D. An event-based framework for characterizing the evolutionarybehavior of interaction graphs. In: Proceedingsof the 13th ACM SIGKDD international conference on Knowledge discoveryand data mining, 2007, 913―921
O’Madadhain J, Hutchins J, Smyth P. Prediction and ranking algorithms for eventbased networkdata. SIGKDD Explorations Newsletter, 2005, 7(2): 23―30 doi: 10.1145/1117454.1117458
Li M, Chen X, Li X, et al. The similarity metric. IEEE Transactions on Information Theory, 2004, 50(12): 3250―3264 doi: 10.1109/TIT.2004.838101
Papadimitriou P, Dasdan A, Garcia-Molina H. Web graph similarity for anomaly detection (poster). In: Proceeding of the 17th International Conferenceon World Wide Web, 2008, 1167―1168
Tomita E, Tanaka A, Takahashi H. The worst-case time complexity for generating all maximalcliques. Computing and Combinatorics: 10thAnnual International Conference on Computing and Combinatorics, 2006, 363(1): 28―42