Weighted Frequent Subgraph Mining in Weighted Graph Databases

Abstract

We focus on the problem of pattern discovery from externally and internally weighted labeled graphs because the target data can be modeled more naturally and in detail by using weighted graphs. For example, while external weight can be used for representing a degree of importance and reliability of a graph itself, internal weight reflects utility and significance of each component in a graph. Therefore, we can expect to realize more precise knowledge discovery by employing weighted graphs. From these backgrounds, in this paper, we discuss two pattern mining problems with external and internal weighted frequencies, and propose two algorithms to solve them efficiently.