A New Polynomial-time Support Measure for Counting Frequent Patterns in Graphs

Frequent subgraph mining (FSM) from graphs is an active subject in computer science research. One major challenge in FSM is the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Current state-of-the-art in this topic features a hypergraph-based framework for modeling pattern occurrences which unifies the two main flavors of support measures: the overlap-graph based maximum independent set measure (MIS) and minimum image/instance based (MNI) measures. For the purpose of exploring the middle ground between these two groups, we introduce a new polynomial-time support measure, called maximum independent subedge set (MISS) measure to fill the gap between MIS and MI in terms of computation complexity and support count. Bounding theorems among all relevant support measures are also presented in this paper.