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...
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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。
To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach co...
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To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.
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