【摘要】 针对分布式k团社区检测引起的超大社区问题,提出了具有节点退出机制的τ-window社区检测方法,相应提出了τ-、window中心性估计。通过实验发现τ-window社区和τ-window中心性具有周期演化特性,利用该特性,提出TTL(time to live)社区检测和TTL中心性估计,以更准确预测消息生存期上节点的相遇。随后,利用TTL社区和TTL中心性作为转发测度,设计了新的机会移动网络路由算法PerEvo。实验结果表明,与现有的基于社会特征的路由算法比较,PerEvo在保持基本不变的传输开销的同时,有效提高了机会移动网络消息投递的成功率。更多还原

【Abstract】 To avoid monster community problem which suffered by distributed κ-clique community detection,(?)-window community detection was proposed.In addition,(?)-window centrality estimation was put forward.By investigating the periodic evolution of(?)-window community and(?)-window centrality,two new metrics,TTL(time to live) community and TTL centrality,were proposed to improve the prediction of the node’s encounter during the message’s lifetime.Moreover,a social-aware routing algorithm,PerEvo,was then designed based on them.Extensive trace-driven simulation results show that PerEvo achieves higher message delivery ratio than the existing social-based forwarding schemes,while keeping similar routing overhead.更多还原