Graph Mining based on a Data Partitioning Approach

Existing graph mining algorithms typically assume that the dataset can fit into main memory. As many large graph datasets cannot satisfy this condition, truly scalable graph mining remains a challenging computational problem. In this paper, we present a new horizontal data partitioning framework for graph mining. The original dataset is divided into fragments, then each fragment is mined individually and the results are combined together to generate a global result. One of the challenging problems in graph mining is about the completeness because the of complexity graph structures. We will prove the completeness of our algorithm in this paper. The experiments will be conducted to illustrate the efficiency of our data partitioning approach.