Content-driven detection of campaigns in social media
Proceedings of the 20th ACM international conference on Information and knowledge management
Fast algorithms for maximal clique enumeration with limited memory
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Web Browsing Log by Using Relaxed Biclique Enumeration Algorithm in MapReduce
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Maximal clique enumeration for large graphs on hadoop framework
Proceedings of the first workshop on Parallel programming for analytics applications
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Structure mining plays an important part in the researches in biology, physics, internet or telecommunications in recently emerging network science. As a main task in this area, the problem of maximal clique enumeration has attracted much interest and been studied in variant avenues in prior works. However, most of these works mainly rely on single chip computational capacity and have been constrained by local optimization. Thus it is an impossible mission for these methods to process terabytes datasets. In this paper, to extract maximal cliques from graphs, we propose a general enumeration process in a distributed manner on cluster system with the help of MapReduce. Graph is firstly split into small subgraphs automatically. Then a novel key-based clique enumeration algorithm is proposed based on subgraphs. We demonstrate that our algorithm has a high parallelism and a prominent performance on extremely huge graphs. Our method is implemented to fully utilize MapReduce execution mechanism and the experiments are soundly discussed as using such a powerful distributed platform. However we not only show the scalability and efficiency of the algorithm but also share some critical experience in using MapReduce computing model.