scholarly journals Similarity-based local community detection for bipartite networks

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1559-1570 ◽  
Author(s):  
Dongming Chen ◽  
Wei Zhao ◽  
Dongqi Wang ◽  
Xinyu Huang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Local Community ◽  
Partial Information ◽  
Real Data ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Local Community Detection

Local community detection aims to obtain the local communities to which target nodes belong, by employing only partial information of the network. As a commonly used network model, bipartite applies naturally when modeling relations between two different classes of objects. There are three problems to be solved in local community detection, such as initial core node selection, expansion approach and community boundary criteria. In this work, a similarity based local community detection algorithm for bipartite networks (SLCDB) is proposed, and the algorithm can be used to detect local community structure by only using either type of nodes of a bipartite network. Experiments on real data prove that SLCDB algorithms output community structure can achieve a very high modularity which outperforms most existing local community detection methods for bipartite networks.

Uncovering local community structure on line graph through degree centrality and expansion

2021 ◽  
pp. 2150120
Author(s):  
Guishen Wang ◽  
Kaitai Wang ◽  
Hongmei Wang ◽  
Huimin Lu ◽  
Xiaotang Zhou ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Local Community ◽  
Line Graph ◽  
Detection Methods ◽  
Degree Centrality ◽  
Original Graph ◽  
Normalized Mutual Information ◽  
Local Community Detection ◽  
On Line

Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.

scholarly journals Is it correct to project and detect? How weighting unipartite projections influences community detection

2020 ◽  
Vol 8 (S1) ◽  
pp. S145-S163 ◽  
Author(s):  
Tristan J. B. Cann ◽  
Iain S. Weaver ◽  
Hywel T. P. Williams
Keyword(s):  
Social Media ◽  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Algorithm Performance ◽  
Projection Scheme ◽  
Community Detection Algorithm ◽  
Experimental Findings

AbstractBipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

A fast local community detection algorithm in complex networks

2021 ◽  
Author(s):  
Zhikang Tang ◽  
Yong Tang ◽  
Chunying Li ◽  
Jinli Cao ◽  
Guohua Chen ◽  
...  
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Local Community Detection ◽  
Community Detection Algorithm

scholarly journals Local Community Detection Algorithm Based on Minimal Cluster

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Zhou ◽  
Guibin Sun ◽  
Yan Xing ◽  
Ranran Zhou ◽  
Zhixiao Wang
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Single Node ◽  
Initial Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
Two Phases ◽  
Community Detection Algorithm

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

scholarly journals Modelling community structure and temporal spreading on complex networks

2020 ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods ◽  
Influence Spreading

Abstract We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. We illustrate the community detection methods with two small network topologies. In case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

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