Determining Leaders and Communities on Networks Using Neighborhood Similarity

Networks are used to represent interactions in a wide variety of fields, like biology, sociology, chemistry, and more. They have a great deal of salient information contained in their structures, which have a variety of applications. One of the important topics of network analysis is finding influential nodes. These nodes are of two kinds —leader nodes and bridge nodes. In this study, we propose an algorithm to find strong leaders in a network based on a revision of neighborhood similarity. This leadership detection is combined with a neighborhood intersection clustering algorithm to produce high quality communities for various networks. We also delve into the structure of a new network, the Houghton College Twitter network, and examine the discovered leaders and their respective followers in more depth than which is frequently attempted for a network of its size. The results of the observations on this and other networks demonstrate that the community partitions found by this algorithm are very similar to those of ground truth communities.

一种基于SDN的ICN高效缓存机制

内置缓存是信息中心网络(ICN)架构最核心的思想之一。为了充分、高效地利用全网缓存资源,提出了一种基于SDN的ICN高效缓存机制(SIC)。该方法利用SDN控制面感知全网状态,在SDN控制面中根据ICN缓存节点的网络社团重要度特性以及社团节点重要度特性来生成缓存策略,使不同流行度的内容对象在各社团之间和同一社团内节点上的分布更合理。在多种实验条件下对SIC策略进行了仿真验证,结果表明与Hash+LRU、Betw+LRU相比,该策略能更好地提升缓存命中率、跳数减少率、平均社团经过个数,同时SDN的资源开销保持在较低的水平。

基于多指标综合的复杂网络节点重要性评估

为全面综合评估节点的重要性,提出一种基于D-S证据理论的多指标综合的复杂网络节点重要性评估方法。通过对复杂网络基本理论的研究,定义以度中心性、接近中心性、介数中心性为代表的显著性评估指标和以韧性度、凝聚度为基础的破坏性评估指标,从各个角度描述节点的重要性;在此基础上,利用D-S证据理论在融合多数据源方面的优势,建立多指标综合的辨识框架和概率分配函数,用Dempster合成规则实现节点多指标的综合评估;综合评估结果排名越靠前,节点越重要。实例分析结果表明,该方法综合了多指标的优势,全面准确地刻画了节点的重要性,为进一步有效利用节点奠定了基础。

基于贡献矩阵的物流配送网络节点重要性评估方法

为了准确识别出物流配送网络中的关键节点,文中提出了基于贡献矩阵的加权网络节点重要度评估的新方法。该方法通过考虑节点的静态结构特征和动态流维度重要性,构建了一个二维重要性指标。据此,利用邻居节点的重要性贡献值表征节点的局部重要性,效率值表征节点的全局重要性,从而确定了最终的重要性评价指标。最后,对某快递企业的物流配送网络进行实证分析,结果验证了文中方法和指标的有效性。