scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

scholarly journals Algorithm for Detecting Communities in Complex Networks Based on Hadoop

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1382 ◽  
Author(s):  
Mo Hai  ◽  
Haifeng Li ◽  
Zhekun Ma ◽  
Xiaomei Gao
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Rapid Analysis ◽  
Experimental Results ◽  
Detection Algorithms ◽  
Accurate Detection ◽  
Hadoop Platform

With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the Hadoop platform (called Community Detection on Hadoop (CDOH)) is proposed in this paper. Based on the basic idea of modularity increment, our algorithm implements parallel merging and accomplishes a fast and accurate detection of the community structure in complex networks. Our extensive experimental results on three real datasets of complex networks demonstrate that the CDOH algorithm can improve the efficiency of the current memory-based community detection algorithms significantly without affecting the accuracy of the community detection.

Enhancing betweenness algorithm for detecting communities in complex networks

2014 ◽  
Vol 28 (09) ◽  
pp. 1450074 ◽  
Author(s):  
Benyan Chen ◽  
Ju Xiang ◽  
Ke Hu ◽  
Yi Tang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Topological Property ◽  
Edge Weight ◽  
Detection Algorithms ◽  
Improved Algorithm

Community structure is an important topological property common to many social, biological and technological networks. First, by using the concept of the structural weight, we introduced an improved version of the betweenness algorithm of Girvan and Newman to detect communities in networks without (intrinsic) edge weight and then extended it to networks with (intrinsic) edge weight. The improved algorithm was tested on both artificial and real-world networks, and the results show that it can more effectively detect communities in networks both with and without (intrinsic) edge weight. Moreover, the technique for improving the betweenness algorithm in the paper may be directly applied to other community detection algorithms.

scholarly journals On community structure in complex networks: challenges and opportunities

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Hocine Cherifi ◽  
Gergely Palla ◽  
Boleslaw K. Szymanski ◽  
Xiaoyan Lu
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Generative Models ◽  
Networked Systems ◽  
Evolving Networks ◽  
Detection Algorithms ◽  
Immunization Strategies ◽  
Challenges And Opportunities ◽  
The Impact

AbstractCommunity structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we follow with an overview of the Stochastic Block Model and its different variants as well as inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.

scholarly journals A Local Overlapping Community Detection Method in Complex Networks

2012 ◽  
Vol 6-7 ◽  
pp. 985-990
Author(s):  
Yan Peng ◽  
Yan Min Li ◽  
Lan Huang ◽  
Long Ju Wu ◽  
Gui Shen Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Experimental Results ◽  
Overlapping Community Detection ◽  
Greedy Strategy ◽  
Structure Detection ◽  
Overlapping Community

Community structure detection has great importance in finding the relationships of elements in complex networks. This paper presents a method of simultaneously taking into account the weak community structure definition and community subgraph density, based on the greedy strategy for community expansion. The results are compared with several previous methods on artificial networks and real world networks. And experimental results verify the feasibility and effectiveness of our approach.

scholarly journals Community Detection by Node Betweenness and Similarity in Complex Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chong Feng ◽  
Jianxu Ye ◽  
Jianlu Hu ◽  
Hui Lin Yuan
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Community Detection ◽  
Experimental Results ◽  
Detection Algorithms ◽  
Simulation Results ◽  
Novel Algorithm

Community detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and gives the detailed steps of the algorithm and simulation results. We compare the proposed algorithm with a series of typical algorithms through experiments on synthetic and actual networks. Experimental results on artificial and real networks demonstrate the effectiveness and superiority of our algorithm.

Using functional dependency and node closure to detect communities from the sparse network

2018 ◽  
Vol 32 (03) ◽  
pp. 1850018
Author(s):  
Yang Zhou ◽  
Haifei Miao ◽  
Wei Liu ◽  
Xiaoyun Chen ◽  
Jianjun Cheng
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Time Complexity ◽  
Functional Dependency ◽  
Experimental Results ◽  
Time Consumption ◽  
Database Theory ◽  
Effectiveness And Efficiency

Community structure is one of the most important features of complex networks, a large number of methods have been proposed to extract community structures from networks. However, some of those methods suffer from the high time complexity, and some of them cannot obtain the acceptable results. In this paper, we borrow the idea from the database theory, and propose the concepts of functional dependency (FD) between nodes and node closure first, then we utilize these concepts to extract communities. This method takes both effectiveness and efficiency into consideration, the community detection process can be accomplished with O(m) time consumption. We conducted extensive experiments both on some synthetic networks and on some real-world networks, the experimental results demonstrate that the method can detect communities from a given network successfully.

Community detection based on gravitational coefficient in collaboration network

2020 ◽  
Vol 34 (14) ◽  
pp. 2050143
Author(s):  
Wen Zhou ◽  
Shuaiqin Zhao
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Clustering Algorithms ◽  
Similarity Index ◽  
Collaborative Networks ◽  
Collaboration Network ◽  
Collaborative Relationship ◽  
Detection Algorithms ◽  
Special Complex

One important characteristic of complex networks is community structure. How to effectively divide the potential community structure of complex networks has been the focus of scholars because communities may have very different properties than the network. A community is usually defined as a collection of nodes with similar attributes. Generally, nodes in the same community are relatively densely connected to each other, compared with nodes from different communities. From the perspective of clustering, nodes in the same community can be considered as having higher similarities. Therefore, using graph clustering algorithms for community detection is theoretically feasible. Collaborative networks are special complex networks. A collaborative relationship tends to connect to multiple collaborators, which makes it hard to build collaborative networks by abstracting the collaboration into edges. Based on characteristics of the collaborative network, we expand the cluster similarity index and propose a gravitational coefficient index to measure the similarity of nodes and subsequently design community detection algorithms. Experiments using real datasets show that the proposed algorithm can obtain higher quality community partitioning results and avoid falling into local optimal solutions to obtain larger-scale communities than classical community detection algorithms.

scholarly journals Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

2021 ◽  
Author(s):  
Jiaxu Liu ◽  
Yingxia Shao ◽  
Sen Su
Keyword(s):  
Community Detection ◽  
Real World ◽  
Local Community ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
High Quality ◽  
Seed Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
High Quality Seed

AbstractLocal community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we first introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node over static networks. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Since dynamic networks also act an important role in practice, we extend the static HqsMLCD to handle dynamic networks and introduce HqsDMLCD. HqsDMLCD mainly integrates dynamic network embedding and dynamic local community detection into the static one. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms. And our dynamic method HqsDMLCD gets comparable results with the static method on real-world 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.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

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