A Divisive Clustering Algorithm for Performance Monitoring of Large Networks using Maximum Common Subgraphs

Abstract

In managing huge-enterprise communication networks, the ability to measure similarity is an important performance monitoring function. It is possible to draw certain significant conclusions regarding effective utilization of networks by characterizing a computer network as a time series of graphs with IP addresses as nodes and communication between nodes as edges. Measuring similarity of graphs is a significant task in mining the graph data for matching, comparing, and evaluating patterns in huge graph databases. The problem of finding the nodes in the communication network which are always active can be formulated as a Maximum Common Subgraph (MCS) detection problem. This paper presents a Divisive Clustering MCS detection algorithm (DC-MCS) to find all maximum comomn subgraphs of k graphs in a graph database. The uniqueness of this algorithm lies in the facts that it considers any number of input graphs can and it scans the graph database only once. The series of experiments performed and the comparison of empirical results with the existing algorithms further ensure the efficiency of the proposed algorithm.