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基于影响力的大规模社会网络快速粗化方法

Fast graph coarsening problem based on influence for large networks
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摘要 给定社会网络,如何快速地粗化社会网络图、是否能够在社会网络图中找到更小的等价表示来保持社会网络的传播特征、是否能够基于节点的影响力属性合并社会网络中的部分节点,这些重要的问题能够应用到影响力分析、流行病学和病毒营销的应用。首先提出了一种新颖的图粗化问题,目的是不改变信息扩散过程中的关键特征来发现图代表节点和边;随后提出了一种快速有效的算法来解决图粗化问题。实验构造在多个真实的数据上,验证了算法的性能和可扩展性,且实验在没有损失图信息的情况下,将图规模降低了90%。 The graph could be quickly zoom-out for a social network. A smaller equivalent graph was got to preserve its propa- gation characteristics. Nodes pairs were merged based on influence properties. Graph coarsening was an important problems with applications to influence analysis, epidemiology and viral marketing applications. This paper proposed a novel graph coarsening problem to find an approximate graph to preserve key characteristics for propagation processes on the graph. Then it proposed a fast and effective near-linear time algorithm to tackle this problem. Experiments were conducted on multiple real datasets. The results demonstrate the quality and scalability of this method. Experimental results show the graph is reduced by 90% according ＇to this method.
作者 贾建伟 陈崚
机构地区 扬州大学信息工程学院
出处 《计算机应用研究》 CSCD 北大核心 2016年第11期3232-3234,3244,共4页 Application Research of Computers
基金 国家自然科学基金面上资助项目(61070240)
关键词 图挖掘 信息传播 社会网络 扩散性 图粗化 graph mining propagation social networks diffusion graph coarsening
分类号 TP393 [自动化与计算机技术—计算机应用技术]
作者简介 贾建伟（1974-），男，江苏扬州人，讲师，硕士，主要研究方向为数据挖掘、计算机技术应用及计算物理（jiajianweixz@163．com）； 陈峻（1951-），男，江苏宝应人，教授，博导，主要研究方向为数据挖掘、人工智能．
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参考文献14

  • 1Dhillon I S, Guan Y, Kulis B. Weighted graph cuts without eigenvee- tors a multilevel approach [ J]. IEEE Trans on Pattern Analysis Machine IntelUgence ,2007,29 ( 11 ) : 1944-1957.
  • 2Elkin M, Peleg D. Approximating k-spanner problems for k > 2 [ J ]. Theoretical Computer Science,2005,337 ( 1- 3 ) :249- 277.
  • 3Fung W S, Hariharan R, Harvey N J,et al. A general framework for graph sparsfication [ C]//Proc of the 43rd Annual ACM Symposium on Theory of Computing. New York:ACM Press ,2011:71- 80.
  • 4Mathioudakis M, Bonchi F, Castillo C, et al. Sparsification of influen- ce networks[ C]//Proc of the 17th ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining. New York: ACM Press,2011:529-537.
  • 5Mehmood Y, Barbieri N, Bonchi F, et al. CSI: community-level so- cial influence analysis [ C ]//Proc of Machine Learning and Knowledge Discovery in Databases. Berlin : Springer-Verlag ,2013 : 125-135.
  • 6Chakrabarti D, Wang Yang, Wang Chenxi, et al. Epidemic thre- sholds in real networks[ J]. ACM Trans on Information and Sys-tem Security,2008,10(4) :20-32.
  • 7Ganesh A, Massoulie L, Towsley D. The effect of network topology on the spread of epidemics [ C ]//Proc of IEEE INFOCOM. Washing- ton DC : IEEE Computer Society,2005:524- 536.
  • 8Pastor-Santorras R, Vespignani A. Epidemic spreading in scale-free networks[ J]. Physical Review Letters,2001,86(14) :411-425.
  • 9Chen Wei, Wang Yajun, Yang Siyu. Efficient influence maximization in social networks[ C]//Proe of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press,2009 : 199-208.
  • 10郭景峰,吕加国.基于信息偏好的影响最大化算法研究[J].计算机研究与发展,2015,52(2):533-541. 被引量：7

二级参考文献39

  • 1Domingos P, Richardson M. Mining the network value of customers[C]//Proc of the 7th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York, ACM, 2001, 57-66.
  • 2Richardson M, Domingos P. Mining knowledge-sharing sites for viral marketing[C] /lProc of the 8th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York, ACM, 2002, 61-70.
  • 3Kempe D, Kleinberg J, Tardos e. Maximizing the spread of influence through a social network[C]//Proc of the 9th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York, ACM, 2003: 137-146.
  • 4Chen W, Wang C, Wang Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks[C]//Proc of the 16th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2010: 1029- 1038.
  • 5Chen W, Wang Y, Yang S. Efficient influence maximization in social networks[C]//Proc of the 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2009: 199-208.
  • 6Chen W, Yuan Y, Zhang L. Scalable influence maximization in social networks under the linear threshold model[C]// Proc of the 10th Int Conf on Data Mining (ICDM). Piscataway, NJ: IEEE, 2010, 88-97.
  • 7Kimura M, Saito K. Tractable models for information diffusion in social networks[C]//Proc of the Knowledge Discovery in Databases (PKDD 2006). Berlin: Springer, 2006, 259-271.
  • 8Leskovec J, Krause A, Guestrin C, et al. Cost-effective outbreak detection in networks[C]//Proc of the 13th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2007: 420-429.
  • 9Goyal A, Lu W, Lakshmanan L. Celf++, Optimizing the greedy algorithm for influence maximization in social networks[C]//Proc of the 20th Int Conf Companion on World Wide Web. New York, ACM, 2011: 47-48.
  • 10Narayanam R, Narahari Y. A shapley value-based approach to discover influential nodes in social networks[J]. IEEE Trans on Automation Science and Engineering, 2011, 8(1): 130-147.

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