Abstract
Graph mining plays an important part in the researches of data mining, and it is widely used in biology, physics, telecommunications and Internet in recently emerging network science. Subgraph mining is a main task in this area, and it has attracted much interest. However, with the growth of graph datasets, most of these former works which mainly rely on single chip computational capacity, cannot process massive graphs. In this paper, we propose a distributed method in solving subgraph mining problems with the help of MapReduce, which is an efficient method of computing. The candidate subgraphs are reduced efficiently according to the degrees of nodes in graphs. The results of our research show that the algorithm is efficient and scalable, and it is a better solution of subgraph mining in extreme large graphs.
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Wu, B., Bai, Y. (2010). An Efficient Distributed Subgraph Mining Algorithm in Extreme Large Graphs. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_14
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DOI: https://doi.org/10.1007/978-3-642-16530-6_14
Publisher Name: Springer, Berlin, Heidelberg
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