Network community detection from the perspective of time series

2019 ◽  
Vol 522 ◽  
pp. 205-214 ◽  
Author(s):  
Dong Wang ◽  
Yi Zhao
Keyword(s):  
Time Series ◽  
Community Detection ◽  
Network Community

Community Detection in Political Discussions on Twitter

2016 ◽  
Vol 35 (2) ◽  
pp. 244-261 ◽  
Author(s):  
Frederic Guerrero-Solé
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Reliable Method ◽  
Community Structures ◽  
Network Community ◽  
High Polarization

In November 9, 2014, the Catalan government called Catalan people to participate in a straw poll about the independence of Catalonia from Spain. This article analyzes the use of Twitter between November 8 and 10, 2014. Drawing on a methodology developed by Guerrero-Solé, Corominas-Murtra, and Lopez-Gonzalez, this work examines the structure of the retweet overlap network (RON), formed by those users whose communities of retweeters have nonzero overlapping, to detect the community structure of the network. The results show a high polarization of the resulting network and prove that the RON is a reliable method to determinate network community structures and users’ political leaning in political discussions.

Time series pattern identification by hierarchical community detection

2021 ◽  
Author(s):  
Leandro Anghinoni ◽  
Didier A. Vega-Oliveros ◽  
Thiago Christiano Silva ◽  
Liang Zhao
Keyword(s):  
Time Series ◽  
Community Detection ◽  
Pattern Identification

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

2018 ◽  
Vol 29 (02) ◽  
pp. 1850011 ◽  
Author(s):  
Chun Gui ◽  
Ruisheng Zhang ◽  
Zhili Zhao ◽  
Jiaxuan Wei ◽  
Rongjing Hu
Keyword(s):  
Community Detection ◽  
Maximum Degree ◽  
Label Propagation ◽  
General Process ◽  
Network Community ◽  
Propagation Algorithm ◽  
Community Belonging ◽  
Original Label ◽  
The Stability ◽  
Detection Quality

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

scholarly journals Exploring the solution landscape enables more reliable network community detection

2019 ◽  
Vol 100 (5) ◽  
Author(s):  
Joaquín Calatayud ◽  
Rubén Bernardo-Madrid ◽  
Magnus Neuman ◽  
Alexis Rojas ◽  
Martin Rosvall
Keyword(s):  
Community Detection ◽  
Network Community

scholarly journals Significance-based multi-scale method for network community detection and its application in disease-gene prediction

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0227244
Author(s):  
Ke Hu ◽  
Ju Xiang ◽  
Yun-Xia Yu ◽  
Liang Tang ◽  
Qin Xiang ◽  
...  
Keyword(s):  
Community Detection ◽  
Disease Gene ◽  
Gene Prediction ◽  
Disease Gene Prediction ◽  
Multi Scale ◽  
Scale Method ◽  
Network Community
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