社区结构是复杂网络中的重要研究领域,也是复杂网络的重要特征之一,网络中的社区结构发现在理解网络功能方面有着重大意义。给定一个大规模异质信息网络,局部社区发现的目标是找到一个包含查询结点的同质社区,并最大化或最小化一种度量...社区结构是复杂网络中的重要研究领域,也是复杂网络的重要特征之一,网络中的社区结构发现在理解网络功能方面有着重大意义。给定一个大规模异质信息网络,局部社区发现的目标是找到一个包含查询结点的同质社区,并最大化或最小化一种度量。本文研究了异质信息网络中的局部社区发现算法,提出了一个新的局部社区发现算法框架(Heterogeneous Local Community Detection)HLCD。该框架主要采用了基于元路径的相似性度量算法HeteSim,来测量与查询结点类型相同的结点之间的相似性,通过随机游走给各个结点赋予权值,并用这些结点权值及结点之间的相似性来重新建图,最后在新图中找到局部社区。展开更多
The increased capacity and availability of the Intemet has led to a wide variety of applications. Intemet traffic characterization and application identification is important for network management. In this paper, bas...The increased capacity and availability of the Intemet has led to a wide variety of applications. Intemet traffic characterization and application identification is important for network management. In this paper, based on detailed flow data collected from the public networks of Intemet Service Providers, we construct a flow graph to model the interactions among users. Considering traffic from different applications, we analyze the community structure of the flow graph in terms of cormmunity size, degree distribution of the community, community overlap, and overlap modularity. The near linear time community detection algorithm in complex networks, the Label Propagation Algorithm （LPA）, is extended to the flow graph for application identification. We propose a new initialization and label propagation and update scheme. Experimental results show that the proposed algorithm has high accuracy and efficiency.展开更多
文摘社区结构是复杂网络中的重要研究领域,也是复杂网络的重要特征之一,网络中的社区结构发现在理解网络功能方面有着重大意义。给定一个大规模异质信息网络,局部社区发现的目标是找到一个包含查询结点的同质社区,并最大化或最小化一种度量。本文研究了异质信息网络中的局部社区发现算法,提出了一个新的局部社区发现算法框架(Heterogeneous Local Community Detection)HLCD。该框架主要采用了基于元路径的相似性度量算法HeteSim,来测量与查询结点类型相同的结点之间的相似性,通过随机游走给各个结点赋予权值,并用这些结点权值及结点之间的相似性来重新建图,最后在新图中找到局部社区。
基金the National Natural Science Foundation of China under Grant No.61171098，the Fundamental Research Funds for the Central Universities of China，the 111 Project of China under Grant No.B08004
文摘The increased capacity and availability of the Intemet has led to a wide variety of applications. Intemet traffic characterization and application identification is important for network management. In this paper, based on detailed flow data collected from the public networks of Intemet Service Providers, we construct a flow graph to model the interactions among users. Considering traffic from different applications, we analyze the community structure of the flow graph in terms of cormmunity size, degree distribution of the community, community overlap, and overlap modularity. The near linear time community detection algorithm in complex networks, the Label Propagation Algorithm （LPA）, is extended to the flow graph for application identification. We propose a new initialization and label propagation and update scheme. Experimental results show that the proposed algorithm has high accuracy and efficiency.
