MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network

Authors:
Yang Liu;Xiaohong Jiang;Huajun Chen;Jun Ma;Xiangyu Zhang
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, China 310027;College of Computer Science, Zhejiang University, Hangzhou, China 310027;College of Computer Science, Zhejiang University, Hangzhou, China 310027;College of Computer Science, Zhejiang University, Hangzhou, China 310027;College of Computer Science, Zhejiang University, Hangzhou, China 310027
Venue:
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
Year:
2009

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Abstract

Network motifs are basic building blocks in complex networks. Motif detection has recently attracted much attention as a topic to uncover structural design principles of complex networks. Pattern finding is the most computationally expensive step in the process of motif detection. In this paper, we design a pattern finding algorithm based on Google MapReduce to improve the efficiency. Performance evaluation shows our algorithm can facilitates the detection of larger motifs in large size networks and has good scalability. We apply it in the prescription network and find some commonly used prescription network motifs that provide the possibility to further discover the law of prescription compatibility.