全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

一种面向语义重叠社区发现的Block场取样算法

DOI: 10.16383/j.aas.2015.c140136, PP. 362-375

Keywords: 语义社会网络,重叠社区,LDA模型,社区发现

Full-Text   Cite this paper   Add to My Lib

Abstract:

?语义社会网络(Semanticsocialnetwork,SSN)是一种包含信息节点及社会关系构成的新型复杂网络.传统语义社会网络分析算法在进行社区挖掘时,需要预先设定社区个数且无法发现重叠社区.针对这一问题,提出一种面向语义重叠社区发现的block场采样算法,该算法首先以LDA(Latentdirichletallocation)模型为语义分析模型,建立了以取样节点为核心节点的block场BAT(Block-author-topic)模型;其次,根据节点的语义分析结果,建立可度量block区域的语义凝聚力方法,实现了语义信息的可度量化;最后,以节点的语义凝聚力为输入,改进了重叠社区发现的标签传播算法(Labelpropagationalgorithm,LPA)及可评价语义社区的SQ度量模型,并通过实验分析,验证了本文算法及SQ度量模型的有效性及可行性.

References

[1]  Yang Bo, Liu Da-You, Liu Jinming, Jin Di, Ma Hai-Bin. Complex network clustering algorithms. Journal of Software, 2009, 20(1): 54-66(杨博, 刘大有, Liu Jinming, 金弟, 马海宾. 复杂网络聚类方法. 软件学报, 2009, 20(1): 54-66)
[2]  Girvan M, Newman M E J. Community structure in social and biological networks. Proceedings of National Academy of Science of the United States of America, 2002, 99(12): 7821-7826
[3]  Newman M E J. Fast algorithm for detecting community structure in networks. Physical Review E, 2004, 69(6): 066133
[4]  Palla G, Derenyi I, Farkas I, Vicsde T. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 2005, 435(7043): 814-818
[5]  Shen H W, Cheng X Q, Cai K, Hu M B. Detect overlapping and hierarchical community structure in networks. Physica A: Statistical Mechanics and Its Applications, 2009, 388(8): 1706-1712
[6]  Lancichinetti A, Fortunato S, Kertesz J. Detecting the overlapping and hierarchical community structure in complex networks.New Journal of Physics, 2009, 11(3): 033015-27
[7]  Gregory S. Finding overlapping communities in networks by label propagation. New Journal of Physics, 2010, 12(10): 103018
[8]  Jin D, Yang B, Baquero C, Liu D Y, He D X, Liu J. A Markov random walk under constraint for discovering overlapping communities in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2011, 2011(5): P05031-21
[9]  Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993-1022
[10]  Zhang H Z, Qiu B J, Giles C L, Foley H C, Yen J. An LDA-based community structure discovery approach for large-scale social networks. In: Proceedings of the 2007 IEEE on Intelligence and Security Informatics. New Brunswick, NJ: IEEE, 2007. 200-207
[11]  Kemp C, Tenenbaum J B, Griffiths T L, Yamada T, Ueda N. Learning systems of concepts with an infinite relational model. In: Proceedings of the 21st National Conference on Artificial Intelligence. California: AAAI Press 2006. 381- 388
[12]  Henderson K, Eliassi R T. Applying latent dirichlet allocation to group discovery in large graphs. In: Proceedings of the 2009 ACM Symposium on Applied Computing. Honolulu, Hawaii, USA: ACM, 2009. 1456-1461
[13]  Henderson K, Eliassi R T, Papadimitriou S, Faloutsos C. HCDF: A hybrid community discovery framework. In: Proceedings of the 2010 SIAM Conference. SDM. 2010. 754-765
[14]  Zhang H Z, Giles C L, Foley H C, Yen J. Probabilistic community discovery using hierarchical latent Gaussian mixture model. In: Proceedings of the 22nd National Conference on Artificial Intelligence. Vancouver, Canada: AAAI, 2007. 663 -668
[15]  Zhang H Z, Li W, Wang X R, Giles C L, Foley H C, Yen J. HSN-PAM: finding hierarchical probabilistic groups from large-scale networks. In: Proceedings of the 7th IEEE International Conference on Data Mining Workshops. Omaha, Nebraska, USA: IEEE, 2007. 27-32
[16]  Steyvers M, Smyth P, Rosen Z M, Griffiths T. Probabilistic author-topic models for information discovery. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2004. 306-315
[17]  McCallum A, Corrada-Emmanuel A, Wang X. Topic and role discovery in social networks. Computer Science Department Faculty Publication Series, 2005, 3(1): 1-7
[18]  McCallum A, Wang X R, Corrada-Emmanuel A. Topic and role discovery in social networks with experiments on Enron and academic email. Journal of Artificial Intelligence Research, 2007, 30(1): 249-272
[19]  Zhou D, Manavoglu E, Li J, Giles C L, Zha H Y. Probabilistic models for discovering e-communities. In: Proceedings of the 15th International Conference on World Wide Web. Edinburgh, Scotland, UK: ACM, 2006. 173-182
[20]  Cha Y, Cho J. Social-network analysis using topic models. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2012. 565-574
[21]  Wang X, Mohanty N, McCallum A. Group and topic discovery from relations and text. In: Proceedings of the 3rd International Workshop on Link Discovery. New York, USA: ACM, 2005. 28-35
[22]  Pathak N, DeLong C, Banerjee A, Erickson K. Social topic models for community extraction. In: Proceedings of the 2nd SNA-KDD Workshop. Las Vegas, Nevada, USA: ACM, 2008. 1-10
[23]  Mei Q Z, Cai D, Zhang D, Zhai C X. Topic modeling with network regularization. In: Proceedings of the 17th International Conference on World Wide Web. Beijing, China: ACM, 2008. 101-110
[24]  Sachan M, Contractor D, Faruquie T A, Subramaniam V. Probabilistic model for discovering topic based communities in social networks. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2011. 2349-2352
[25]  Sachan M, Contractor D, Faruquie T A, Subramaniam V L. Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web. New York, USA: ACM, 2012. 331-340
[26]  Yin Z J, Cao L L, Gu Q Q, Han J W. Latent community topic analysis: integration of community discovery with topic modeling. ACM Transactions on Intelligent Systems and Technology, 2012, 3(4): 63
[27]  Xie J R, Szymanski B K, Liu X M. SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: Proceedings of the 11th IEEE International Conference of Data Mining Workshops. Vancouver, BC: IEEE, 2011. 344-349
[28]  Jin Di, Yang Bo, Liu Jie, Liu Da-You, He Dong-Xiao. Ant colony optimization based on random walk for community detection in complex networks. Journal of Software, 2012, 23(3): 451-464(金弟, 杨博, 刘杰, 刘大有, 何东晓. 复杂网络簇结构探测 --- 基于随机游走的蚁群算法. 软件学报, 2012, 23(3): 451-464)
[29]  Gan Wen-Yan, He Nan, Li De-Yi, Wang Jian-Min. Community discovery method in networks based on topological potential. Journal of Software, 2009, 20(8): 2241-2254(淦文燕, 赫南, 李德毅, 王建民. 一种基于拓扑势的网络社区发现方法, 软件学报, 2009, 20(8): 2241-2254)
[30]  Jin Di, Liu Jie, Yang Bo, He Dong-Xiao, Liu Da-You. Genetic algorithm with local search for community detection in large-scale complex networks. Acta Automatica Sinica, 2011, 37(7): 873-882(金弟, 刘杰, 杨博, 何东晓, 刘大有. 局部搜索与遗传算法结合的大规模复杂网络社区探测. 自动化学报, 2011, 37(7): 873-882)
[31]  He Dong-Xiao, Zhou Xu, Wang Zuo, Zhou Chun-Guang, Wang Zhe, Jin Di. Community mining in complex Networks-Clustering combination based genetic algorithm. Acta Automatica Sinica, 2010, 36(8): 1160-1170(何东晓, 周栩, 王佐, 周春光, 王喆, 金弟. 复杂网络社区挖掘—基于聚类融合的遗传算法. 自动化学报, 2010, 36(8): 1160-1170)
[32]  Yang Bo, Liu Jie, Liu Da-You. A random network ensemble model based generalized network community mining algorithm. Acta Automatica Sinica, 2012, 38(5): 812-822(杨博, 刘杰, 刘大有. 基于随机网络集成模型的广义网络社区挖掘算法. 自动化学报, 2012, 38(5): 812-822)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133