微博社区的动态节点自适应增量交互优化

大多数的社区发现方法是基于网络拓扑结构和边缘密度来进行最佳社区确定,但是这些方法具有非常高的计算复杂度,对网络的形式和类型非常敏感。为解决这些问题,提出基于动态节点自适应增量模型的微博社区交互优化算法,该算法在优化每个社区内成员的交互作用的基础上,利用贪婪算法有效地搜索最优社区的候选,无需遍历所有节点。该模型可快速、准确地测量社区内部和社区之间的交互作用差异。最后,在基准测试网络和搜狐微博平台抓取数据上的仿真测试显示,所提算法在召回率、准确率、算法计算时间以及网络覆盖率等指标上,要优于选取的对比算法。

Dynamic nodes adaptive incremental interactive optimization for micro-blog communities

Most community discovery methods determine the best community according to network topology and edge density, however, they have very high computational complexity, and are very sensitive to the form and type of the network. To solve these problems, we propose an interactive optimization algorithm based on dynamic nodes adaptive incremental model for micro-blog communities. By optimizing the interaction among members in each community, the algorithm efficiently searches the candidates of the optimal community without traversing all the nodes by using the greedy algorithm. The model allows rapid and accurate measurement of interaction difference within community and across communities. Finally, simulations on the data grabbed from benchmark networks and Sohu micro blog platform show that the proposed algorithm outperforms other algorithms in recall rate, accuracy, computation time, and network coverage rate.