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Journal of Information Science and Engineering, Vol. 31 No. 4, pp. 1387-1411 (July 2015)

A Novel Group Detection Method for Finding Related Chinese Herbs*

1Hangzhou Normal University
Zhejiang, 310036 P.R. China
2College of Computer Science and Technology
Zhejiang University
Hangzhou, Zhejiang, 310027 P.R. China
3Zhejiang Chinese Medical University
Zhejiang, 310053 P.R. China

In past decades, TCM (Traditional Chinese Medicine) has been widely researched through various methods in computer science, but none digs into huge amount of ancient TCM prescriptions and endless digital TCM information to display the compatible and incompatible relationship among herbs. To meet the challenge and to mine the groups of compatible herbs for further drug exploitation, we explore the property of herbal networks and introduce a novel community detection algorithm concerning both herbal attributes and graph structural factors. First, we calculate the attribute similarity for each paired herbs to construct the herbal graph. Then, a novel community detection algorithm named RWLT (Random Walk & Label Transmission) is proposed to detect herbal groups with near-linear time. The performance of RWLT has been rigorously validated through comparisons with representative methods against randomly created networks, real-world networks and herbal networks. According to the TCM expert, our method is capable of finding groups of Chinese herbs with intensive correlation, and is also able to separate the herbs with mutual incompatibility to be excluded into different communities.

Keywords: herbal group detection, community detection, random walk, label transmission, RWLT algorithm

Full Text () Retrieve PDF document (201507_14.pdf)

Received January 8, 2014; revised May 2 & July 8, 2014; accepted September 22, 2014.
Communicated by Chih-Ping Wei.
* This work was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. Q14- F020032, Chinese Knowledge Center for Engineering Science and Technology (CKCEST), Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (20130101110136), and China Academic Digital Associative Library (CADAL).