中文摘要：

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。

英文摘要：

To avoid monster community problem which suffered by distributed k-clique community detection, τ-window community detection was proposed. In addition, T-window centrality estimation was put forward. By investigating the periodic evolution of τ--window community and τ-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 keep- ing similar routing overhead.