Performance Monitoring of Large Communication Networks using Maximum Common Subgraphs

Measuring similarity of graphs is an important task in graph mining for matching, comparing, and evaluating patterns in huge graph databases. 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. The maximum common subnets of k network time series graphs give a measure of the utilization of network nodes at different intervals of time. 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 which would be useful for various decision making tasks such as devising better routing algorithms. This paper presents a novel MCS detection algorithm that introduces a new heap-based data structure to find all MCS of k graphs in a graph database efficiently. The series of experiments performed and the comparison of empirical results with the existing algorithms further ensure the efficiency of the proposed algorithm.