基于社区结构的影响力最大化算法  

Influence optimization model based on community structure

作　　者：郭进时[1] 汤红波[1] 吴凯[1] 杨森[1] 

机构地区：[1]国家数字交换系统工程技术研究中心,郑州450002

出　　处：《计算机应用》2013年第9期2436-2439,2459,共5页journal of Computer Applications

基　　金：国家863计划项目(2011AA7116031;2011AA010604);国家973计划项目(2012CB315901)

摘　　要：现有的社会网络影响力算法及模型的较高的时间复杂度已不适用于网络规模不断壮大的社会网络服务。针对上述问题,提出了一种基于网络社区结构的影响力最大化算法。首先评估各个社区中节点的影响力,挖掘其核心节点成员;继而在核心节点集和连接社区间的弱纽带节点中选取若干具有影响潜力的初始节点集,使其以最小的代价让信息在网络中得到最广泛的传播。实验结果表明:该算法不仅大大降低了时间复杂度,还获得了接近贪心算法的影响范围,影响覆盖率达到了90%以上。The relatively large time cost of the existing influence algorithms does not fit the social networks of which the scale keeps expanding. An influence optimization model was proposed based on the network community structure for solving problem of large time cost. Firstly evaluate nodes influence in each community and dig core members, and then find a small subset of nodes in the set composed of the core nodes and linking community nodes to get the maximization diffusion with minimization cost. The experimental results demonstrate that our model achieves the subset with more abroad influence diffusion and reduces running time compared with traditional methods. Its influence coverage is up to 90%.

关 键 词：社会网络 影响力 社区结构 弱纽带 信息传播 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]