The problem of discovering frequent subgraphs of graph data can be solved by constructing a candidate set of subgraphs first, and then, identifying within this candidate set those subgraphs that meet the frequent subgraph requirement. In Apriori-based graph mining, to determine candidate subgraphs from a huge number of generated adjacency matrices is usually the dominating factor for the overal...

The data mining has remained a subject of unfailing charm for research. knowledge graph is rising and showing infinite life force strong developing potential in recent years, where it observed that acyclic capacity enhancing usability. Though the development graphs provided an ample scope appearing abilities mining, related researches are still insufficient. In this paper, we introduce path tra...

While extensive work has been done in both graph mining and subgroup discovery, the potential benefits of combining the two fields have not been well studied. We propose, implement, and evaluate an adaption of an existing subgroup discovery algorithm to mine graph data. Our experiments use two different metrics from the subgroup discovery literature to demonstrate value in using such metrics to...