Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Graph Data
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Graph Twiddling in a MapReduce World
Computing in Science and Engineering
Efficient Dense Structure Mining Using MapReduce
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
An iterative MapReduce approach to frequent subgraph mining in biological datasets
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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.