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

Abstract

Summary 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.

The Use of Graph Databases in Systems Biology: A Systematic Review

“…3. Subgraph mining identifies frequently occurring patterns (sub-graphs) in complex graph structures [54]. In systems biology, sub-graph mining is used to identify important molecular interactions and biological pathways in large-scale biological data such as protein-protein interaction networks or metabolic pathways, and to identify coding patterns and overlap of systems biology models [55].…”