Community detector on symptom networks with applications to fatty liver disease

Cong Li, Wenjing Wang, Jingya Li, Jiatuo Xu and Xiang Li

Physica A: Statistical Mechanics and its Applications, 2019, vol. 527, issue C

Abstract: Community detection separates nodes into communities where nodes are densely connected. Here we apply the community detection to find the strongly related nodes in a network, by introducing the local information of nodes, such as the weight strength of nodes and the link weights into the BGLL community detection. We have improved the BGLL community detection algorithms with three methods and compared their qualities of finding the relations between the symptoms and severity levels in symptom networks. The high accuracy of an improved BGLL algorithm in finding the strong relations between the symptoms and the severity levels are verified in the symptom networks of the fatty liver disease. The relations that we have observed motivate us to find a small set of representative symptoms for each severity level of the fatty liver disease. Besides, we achieve the changing trends of the representative symptoms in the fatty liver disease. The results of the representative symptoms and changing trends of the representative symptoms in FLD are supported by clinical diagnosis.

Keywords: Community detection; Complex network; Symptom network; Fatty liver disease; Severity level; Prediction (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307861

DOI: 10.1016/j.physa.2019.121328

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