Zhang Lianming, Peng Aoyuan, Yu Jianping
College of Physics and Information Science, Hunan Normal University, Changsha 410081, China.
College of Physics and Information Science, Hunan Normal University, Changsha 410081, China ; Department of Computer Science and Technology, Hunan University of Humanities, Science and Technology, Loudi 417000, China.
ScientificWorldJournal. 2013 Dec 18;2013:391782. doi: 10.1155/2013/391782. eCollection 2013.
Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based on k-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency.
社交网络往往呈现出一些与规则网络和随机网络不同的拓扑特征,例如平均路径长度较短和聚类系数较高,并且大多数社交网络的节点度服从指数分布。基于真实社交网络的拓扑特征,提出了一种适合描绘社交网络结构的新网络模型,并计算了该模型的特征参数。为了找出社交网络中两人之间的关系,并利用社交网络的局部信息和并行机制,提出了一种基于k步随机游走和高度的混合搜索策略。仿真结果表明,该策略能显著减少平均搜索步数,从而有效提高搜索速度和效率。