GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.
In frequent pattern mining, i think need to input a subgraph pattern(P), a single large graph(G) and a frequency threshold to find the number of occurrences of the subgraph P in G.
Question 2 in the GraMi paper.
So in the GraMi framework, how to input subgraph pattern(P)?
The goal of frequent subgraph mining is to discover frequent subgraphs. The input to FSM is a single large graph (G) and the frequency threshold, and the output is a list of frequent patterns.
ehab-abdelhamid
commented
3 years ago
In frequent pattern mining, i think need to input a subgraph pattern(P), a single large graph(G) and a frequency threshold to find the number of occurrences of the subgraph P in G. Question 2 in the GraMi paper.
So in the GraMi framework, how to input subgraph pattern(P)?