The Application Research of Social Networks Community Detection with Correlation Coefficients
- https://doi.org/10.2991/icence-16.2016.112How to use a DOI?
- algebraic connectivity; matrix reordering; laplascian matrix; correlation coefficients; social networks
In complex network graph, cut model based on edge centrality doesn't apply to overlapping community detection by minimizing the algebraic connectivity of complex networks. The problem can be resolved by calculating node Correlation coefficients. It cuts one edge at one time, which is the fastest decline in the algebraic connectivity, until to divide into two communities. When components of fielder vector is less than threshold level 'a' and the difference of node adjacent edges which belong to two groups is less than 2, correlation coefficients is calculated. This node's community can be detected by the correlation coefficients. We concluded that elapsed time by ordering matrix is less than before. The advanced cut model can be used in overlapping community detection with higher efficiency and accuracy.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Fuqiang Zhao AU - Guijun Yang AU - Enjun Xing AU - Li He PY - 2016/09 DA - 2016/09 TI - The Application Research of Social Networks Community Detection with Correlation Coefficients BT - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) PB - Atlantis Press SP - 588 EP - 593 SN - 2352-538X UR - https://doi.org/10.2991/icence-16.2016.112 DO - https://doi.org/10.2991/icence-16.2016.112 ID - Zhao2016/09 ER -