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

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.

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Similarity-based local community detection for bipartite networks

Filomat ◽

10.2298/fil1805559c ◽

2018 ◽

Vol 32 (5) ◽

pp. 1559-1570 ◽

Cited By ~ 1

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.

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Link community detection based on ensemble learning

Modern Physics Letters B ◽

10.1142/s0217984920502930 ◽

2020 ◽

Vol 34 (27) ◽

pp. 2050293

Author(s):

Zhihua Liu ◽

Hongmei Wang ◽

Guishen Wang ◽

Yu Zhou

Keyword(s):

Ensemble Learning ◽

Community Detection ◽

Clustering Algorithm ◽

Line Graph ◽

Distance Matrix ◽

Original Graph ◽

Overlapping Communities ◽

Normalized Mutual Information ◽

Pca Method ◽

Overlapping Community

Overlapping community detection is a hot topic in the research of data mining and graph theory. In this paper, we propose a link community detection method based on ensemble learning (LCDEL). First, we transform graph into line graph and construct node adjacency matrix of line graph. Second, we calculate node distance of line graph through a new distance metric and get node distance matrix of line graph. Third, we use PCA method to reduce dimensions of node distance matrix of line graph. Then, we cluster on the reduced node distance matrix by k-means clustering algorithm. Finally, we convert line graph back into original graph and get overlapping communities of original graph with ensemble learning. Experimental results on several real-world networks demonstrate effectiveness of LCDEL method in terms of Normalized Mutual Information (NMI), Extended Modularity (EQ) and F-score evaluation metrics.

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How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2014.0224 ◽

2014 ◽

Vol 470 (2170) ◽

pp. 20140224 ◽

Cited By ~ 1

Author(s):

Binh-Minh Bui-Xuan ◽

Nick S. Jones

Keyword(s):

Community Detection ◽

Local Community ◽

Heuristic Algorithms ◽

System Size ◽

Structural Features ◽

Networked Systems ◽

Modular Structure ◽

Original Graph ◽

Graph Optimization ◽

Local Community Detection

By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O (2 n ), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.

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Link community detection combined with network pruning and local community expansion

Modern Physics Letters B ◽

10.1142/s0217984921500986 ◽

2020 ◽

pp. 2150098

Author(s):

Zhihua Liu ◽

Hongmei Wang ◽

Guishen Wang ◽

Zhenjun Guo ◽

Yu Zhou

Keyword(s):

Community Detection ◽

Local Community ◽

Detection Method ◽

Line Graph ◽

Local Communities ◽

Pagerank Algorithm ◽

Network Pruning ◽

On Line ◽

Overlapping Community ◽

Score Matrix

Studying overlapping community structure can help people understand complex network. In this paper, we propose a link community detection method combined with network pruning and local community expansion (NPLCE). Firstly, we delete unattractive links and transform pruned graph into line graph. Secondly, we calculate score matrix on line graph through pagerank algorithm. Then, we search seed nodes and expand local communities from the seed nodes. Finally, we merge those communities and transform them back into node communities. The experiment results on several real-world networks demonstrate the performance of our algorithm in terms of accuracy.

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Approaching the Optimal Solution of the Maximal α-quasi-clique Local Community Problem

Electronics ◽

10.3390/electronics9091438 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1438

Author(s):

Patricia Conde-Cespedes

Keyword(s):

Graph Theory ◽

Community Detection ◽

Local Community ◽

Optimal Solution ◽

Local Communities ◽

Optimal Value ◽

Interesting Task ◽

Local Community Detection ◽

Networks Analysis ◽

Node Problem

Complex networks analysis (CNA) has attracted so much attention in the last few years. An interesting task in CNA complex network analysis is community detection. In this paper, we focus on Local Community Detection, which is the problem of detecting the community of a given node of interest in the whole network. Moreover, we study the problem of finding local communities of high density, known as α-quasi-cliques in graph theory (for high values of α in the interval ]0,1[). Unfortunately, the higher α is, the smaller the communities become. This led to the maximal α-quasi-clique community of a given node problem, which is, the problem of finding local communities that are α-quasi-cliques of maximal size. This problem is NP-hard, then, to approach the optimal solution, some heuristics exist. When α is high (>0.5) the diameter of a maximal α-quasi-clique is at most 2. Based on this property, we propose an algorithm to calculate an upper bound to approach the optimal solution. We evaluate our method in real networks and conclude that, in most cases, the bound is very accurate. Furthermore, for a real small network, the optimal value is exactly achieved in more than 80% of cases.

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Modelling community structure and temporal spreading on complex networks

Computational Social Networks ◽

10.1186/s40649-021-00094-z ◽

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.

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