An algorithm for partitioning community graph into sub-community graphs using graph mining techniques

Abstract

Using graph mining techniques, knowledge extraction is possible from the community graph. In our work, we started with the discussion on related definitions of graph partition both mathematical as well as computational aspects. The derived knowledge can be extracted from a particular sub-graph by way of partitioning a large community graph into smaller sub-community graphs. Thus, the knowledge extraction from the sub-community graph becomes easier and faster. The partition is aiming at the edges among the community members of different communities. We have initiated our work by studying techniques followed by different researchers, thus proposing a new and simple algorithm for partitioning the community graph in a social network using graph techniques. An example verifies about the strength and easiness of the proposed algorithm.