A novel MapReduce-based approach for distributed frequent subgraph mining
Sabeur Aridhi Laurent d’Orazio Mondher Maddouriand Engelbert Mephu Nguifo
In this paper, we propose a no-vel approach to approximate large-scale subgraph mining by means of a density-based partitioning technique, using the MapReduce framework. Our partitioning aims to ba-lance computational load on a collection of machines.
