An Updated Dashboard of Complete Search FSM Implementations in Centralized Graph Transaction Databases

Frequent subgraph mining algorithms are widely used in various areas for complex analysis. As yet, a handful number of algorithms have been proposed in literature. Several experimental studies were reported; however, these experiments lack some critical details which are vital to select an implementation of an algorithm for a specific purpose. In this paper, we discuss an experimental study with implementations of complete search Frequent Subgraph Mining (FSM) algorithms in centralized graph databases.

The main purpose of this experimental study is to find a suitable Frequent Subgraph Mining solution for indexing large graphs databases for aggregated search. Out of 32 algorithms that were found in the literature, only six of them were selected for our experiments through a filtering process which relies on a set of criteria. Thirteen working implementations of these 6 algorithms are experimented. In this paper, we provide details of the experimental results in terms of performance metrics and input variation effect. We propose a selection of the most efficient FSM solutions (i.e., implementations) for end users.