Network Subgraph Pattern Mining

Abstract

Abstract Mining local connection patterns in large-scale networks is of great significance for understanding the structure and function of biological networks and online social networks. In practical applications, there are many problems and challenges, such as unknown network structure, large network scale, large number of subgraphs, and large operation of subgraph pattern analysis, which make it very difficult to analyze the local connection patterns of large-scale network graph accurately and quickly. In order to solve this problem, this paper designs a method of crawler and sampling to obtain the topology of the unknown network. At the same time, the deviation introduced in the process of data acquisition is modeled and analyzed, which can be compensated and corrected. A sampling unbiased estimation method suitable for large-scale static graph and high-speed dynamic flow graph is designed. The research results of this paper can be used to accurately and quickly mine and estimate the eigenvalues of local connection patterns, and provide important technical means for computer network traffic monitoring, online social network analysis and biomolecular network information mining.