scholarly journals Label Propagation withα-Degree Neighborhood Impact for Network Community Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Heli Sun ◽  
Jianbin Huang ◽  
Xiang Zhong ◽  
Ke Liu ◽  
Jianhua Zou ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Structure And Function ◽  
Label Propagation ◽  
Iterative Approach ◽  
Network Community ◽  
Weight Value ◽  
And Function ◽  
Node Labels ◽  
Better Than

Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach withα-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of itsα-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on theα-degree neighborhood impact of all the nodes. Theα-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scopeαcan be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.

Distributed learning automata-based algorithm for community detection in complex networks

2016 ◽  
Vol 30 (08) ◽  
pp. 1650042 ◽  
Author(s):  
Mohammad Mehdi Daliri Khomami ◽  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Learning Automata ◽  
Distributed Learning ◽  
Structure And Function ◽  
Label Propagation ◽  
Information Networks ◽  
Detection Algorithms ◽  
Distributed Learning Automata ◽  
And Function

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

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.

Community Structure Detection Using Firefly Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 52-70 ◽  
Author(s):  
Ameera Saleh Jaradat ◽  
Safa'a Bani Hamad
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Graph Partitioning ◽  
Firefly Algorithm ◽  
Real Life ◽  
People Detection ◽  
Continuous Growth ◽  
Structure Detection ◽  
Partitioning Problem ◽  
And Function

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

scholarly journals The Relationship between the Community Structure and Function of Bacterioplankton and the Environmental Response in Qingcaosha Reservoir

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3155
Author(s):  
Shumin Liu ◽  
Fengbin Zhao ◽  
Xin Fang
Keyword(s):  
Water Quality ◽  
Community Structure ◽  
High Throughput Sequencing ◽  
Scientific Basis ◽  
Vital Role ◽  
Structure And Function ◽  
Environmental Response ◽  
Temporal And Spatial ◽  
Density And Biomass ◽  
And Function

Phytoplankton and bacterioplankton play a vital role in the structure and function of aquatic ecosystems, and their activity is closely linked to water eutrophication. However, few researchers have considered the temporal and spatial succession of phytoplankton and bacterioplankton, and their responses to environmental factors. The temporal and spatial succession of bacterioplankton and their ecological interaction with phytoplankton and water quality were analyzed using 16S rDNA high-throughput sequencing for their identification, and the functions of bacterioplankton were predicted. The results showed that the dominant classes of bacterioplankton in the Qingcaosha Reservoir were Gammaproteobacteria, Alphaproteobacteria, Actinomycetes, Acidimicrobiia, and Cyanobacteria. In addition, the Shannon diversity indexes were compared, and the results showed significant temporal differences based on monthly averaged value, although no significant spatial difference. The community structure was found to be mainly influenced by phytoplankton density and biomass, dissolved oxygen, and electrical conductivity. The presence of Pseudomonas and Legionella was positively correlated with that of Pseudanabaena sp., and Sphingomonas and Paragonimus with Melosira granulata. On the contrary, the presence of Planctomycetes was negatively correlated with Melosira granulata, as was Deinococcus-Thermus with Cyclotella sp. The relative abundance of denitrifying bacteria decreased from April to December, while the abundance of nitrogen-fixing bacteria increased. This study provides a scientific basis for understanding the ecological interactions between bacteria, algae, and water quality in reservoir ecosystems.

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