| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 01021 | |
| Number of page(s) | 4 | |
| Section | Network Security System, Neural Network and Data Information | |
| DOI | https://doi.org/10.1051/matecconf/201823201021 | |
| Published online | 19 November 2018 | |
Comparative Study Of Complex Network Community Structure Algorithms In network Pharmacology Analysis
School of Computer, Jiang Xi University of Traditional Chinese Medicine, NanChang, JiangXi, China
a Corresponding author: xiaoxiongxwp@126.com
Community structure is an extremely important characteristic of complex networks composed of network pharmacology. The mining of network community structure is of great importance in many fields such as biology, computer science and sociology. In recent years, for different types of large-scale complex networks, researchers had proposed many algorithms for finding community structures. This paper reviewed some of the most representative algorithms in the field of network pharmacology, and focused on the analysis of the improved algorithms based on the modularity index and the new algorithms that could reflect the level and overlap of the community. Finally, a benchmark was established to measure the quality of the community classification algorithm.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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