Compact in-memory representation of large graph databases for efficient mining of maximal frequent sub graphs

Complex networks have been used in many scientific disciplines like sociology, microbiology, and telecommunication to represent the interactions among them. Graphs are generally used for representing such complex networks. Mining significant frequent patterns from graph databases has been a challenging area of research. A number of sub graph mining algorithms have been proposed for finding frequent fragments in molecular databases. A very few algorithms have been proposed for mining frequent patterns from large communication networks. All these algorithms perform well on medium size networks and fail on very large graphs. The scalability of these algorithms has been an issue because of the enormous memory requirements and also due to the exponential number of frequent sub graphs possible. In this paper, we propose a compact way of representing graph databases and also use it in a maximal frequent sub graph mining algorithm. The algorithm is found to be efficient and scalable to very large graph databases.

Automated summary

This paper discusses the application of complex networks in various scientific disciplines and the challenges of mining important frequent patterns from graph databases. Existing algorithms perform well on medium networks but struggle with large graphs, whereas the proposed algorithm in this paper is efficient and scalable on very large graph databases.