Coarse graining of complex networks is an important method to study large-scale complex networks, and is also in the focus of network science today. This paper tries to develop a new coarse-graining method for complex...Coarse graining of complex networks is an important method to study large-scale complex networks, and is also in the focus of network science today. This paper tries to develop a new coarse-graining method for complex networks, which is based on the node similarity index. From the information structure of the network node similarity, the coarse-grained network is extracted by defining the local similarity and the global similarity index of nodes. A large number of simulation experiments show that the proposed method can effectively reduce the size of the network, while maintaining some statistical properties of the original network to some extent. Moreover, the proposed method has low computational complexity and allows people to freely choose the size of the reduced networks.展开更多
链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考...链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.展开更多
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 ...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.展开更多
针对曲面场景中的异构无线传感器网络节点定位问题,提出了一种基于相似路径的节点定位算法(Node Localization Algorithm Based on Similar Paths,NLA-SP)。首先,依据Ochiai系数计算锚节点到未知节点的传播路径与各锚节点对间路径的相似...针对曲面场景中的异构无线传感器网络节点定位问题,提出了一种基于相似路径的节点定位算法(Node Localization Algorithm Based on Similar Paths,NLA-SP)。首先,依据Ochiai系数计算锚节点到未知节点的传播路径与各锚节点对间路径的相似值,找出相似路径;其次,根据相似路径对应的锚节点对距离与各单跳路径首节点的通信半径估算锚节点到未知节点的距离;然后,利用融合黄金正弦策略与粒子群优化算法的麻雀算法搜索未知节点的坐标;最后,为减小三维曲面Z轴的坐标误差采用坐标投影法对未知节点的坐标进行校正。仿真结果表明,所提算法较IDV-Hop算法、CPPA算法、HHOMA算法,定位精度明显提高。展开更多
文摘Coarse graining of complex networks is an important method to study large-scale complex networks, and is also in the focus of network science today. This paper tries to develop a new coarse-graining method for complex networks, which is based on the node similarity index. From the information structure of the network node similarity, the coarse-grained network is extracted by defining the local similarity and the global similarity index of nodes. A large number of simulation experiments show that the proposed method can effectively reduce the size of the network, while maintaining some statistical properties of the original network to some extent. Moreover, the proposed method has low computational complexity and allows people to freely choose the size of the reduced networks.
文摘链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.
文摘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.
文摘针对曲面场景中的异构无线传感器网络节点定位问题,提出了一种基于相似路径的节点定位算法(Node Localization Algorithm Based on Similar Paths,NLA-SP)。首先,依据Ochiai系数计算锚节点到未知节点的传播路径与各锚节点对间路径的相似值,找出相似路径;其次,根据相似路径对应的锚节点对距离与各单跳路径首节点的通信半径估算锚节点到未知节点的距离;然后,利用融合黄金正弦策略与粒子群优化算法的麻雀算法搜索未知节点的坐标;最后,为减小三维曲面Z轴的坐标误差采用坐标投影法对未知节点的坐标进行校正。仿真结果表明,所提算法较IDV-Hop算法、CPPA算法、HHOMA算法,定位精度明显提高。
