互联网流量的结构特性:重叠社团和相关性(英文)

There is an increasing number of Internet applications, which leads to an increasing network capacity and availability. Internet traffic characterisation and application identification are, therefore, more important for efficient network management. In this paper, we construct flow graphs from detailed Internet traffic data collected from the public networks of Internet Service Providers. We analyse the community structures of the flow graph that is naturally formed by different applications. The community size, degree distribution of the community, and community overlap of 10 Internet applications are investigated. We further study the correlations between the communities from different applications. Our results provide deep insights into the behaviour Internet applications and traffic, which is helpful for both network management and user behaviour analysis.     

互联网流量识别中的基于标签传播的重叠社团发现算法

The increased capacity and availability of the Internet has led to a wide variety of applications. Internet traffic characterization and application i-dentification is important for network management. In this paper, based on detailed flow data collected from the public networks of Internet Service Pro-viders, we construct a flow graph to model the in-teractions among users. Considering traffic from different applications, we analyze the community structure of the flow graph in terms of community size, degree distribution of the community, commu-nity overlap, and overlap modularity. The near line-ar time community detection algorithm in complex networks, the Label Propagation Algorithm (LPA), is extended to the flow graph for application identi-fication. We propose a new initialization and label propagation and update scheme. Experimental re-sults show that the proposed algorithm has high ac-curacy and efficiency.