scholarly journals An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhixiao Wang ◽  
Ya Zhao ◽  
Zhaotong Chen ◽  
Qiang Niu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Local Maximum ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Network Nodes ◽  
Pagerank Algorithm ◽  
Community Detection Algorithm ◽  
New Community ◽  
Almost All

Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

scholarly journals A Community Detection Algorithm Based on Topology Potential and Spectral Clustering

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhixiao Wang ◽  
Zhaotong Chen ◽  
Ya Zhao ◽  
Shaoda Chen
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Structural Information ◽  
Clustering Algorithms ◽  
Local Maximum ◽  
Laplacian Matrix ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Excellent Performance ◽  
Community Detection Algorithm

Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods.

Air quality analysis of Sichuan province based on complex network and CSP algorithm

2021 ◽  
pp. 2250007
Author(s):  
Xiao Li Huang ◽  
Si Yu Hu ◽  
Jing Xian Chen ◽  
Wan Qi Feng
Keyword(s):  
Air Quality ◽  
Complex Network ◽  
Community Detection ◽  
Network Theory ◽  
Strong Correlation ◽  
Cross Correlation ◽  
Detection Algorithm ◽  
Sichuan Province ◽  
Complex Network Theory ◽  
Community Detection Algorithm

The air quality is directly related to people’s lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control.

Community detection in complex network by network embedding and density clustering

2021 ◽  
pp. 1-12
Author(s):  
JinFang Sheng ◽  
Huaiyu Zuo ◽  
Bin Wang ◽  
Qiong Li
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Dimensional Space ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Network Embedding ◽  
Detection Algorithms ◽  
Density Clustering ◽  
Community Detection Algorithm ◽  
Low Dimensional

 In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

scholarly journals Community Detection Algorithm Based on Intelligent Calculation of Complex Network Nodes

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yanjia Tian ◽  
Xiang Feng
Keyword(s):  
Data Mining ◽  
Community Detection ◽  
Real Life ◽  
Research Direction ◽  
Detection Algorithm ◽  
The Political ◽  
Data Set ◽  
Political Blogs ◽  
Network Nodes ◽  
Community Detection Algorithm

With the explosive development of big data, information data mining technology has also been developed rapidly, and complex networks have become a hot research direction in data mining. In real life, many complex systems will use network nodes for intelligent detection. When many community detection algorithms are used, many problems have arisen, so they have to face improvement. The new detection algorithm CS-Cluster proposed in this paper is derived by using the dissimilarity of node proximity. Of course, the new algorithm proposed in this article is based on the IGC-CSM algorithm. It has made certain improvements, and CS-Cluster has been implemented in the four algorithms of IGC-CSM, SA-Cluster, W-Cluster, and S-Cluster. The result of comparing the density value on the entropy value of the Political Blogs data set, the DBLP data set, the Political Blogs data set, and the entropy value of the DBLP data set is shown. Finally, it is concluded that the CS-Cluster algorithm is the best in terms of the effect and quality of clustering, and the degree of difference in the subgraph structure of clustering.

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

2020 ◽  
Vol 13 (4) ◽  
pp. 542-549
Author(s):  
Smita Agrawal ◽  
Atul Patel
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Connected Subgraph ◽  
Comprehensive Review ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

Sign in / Sign up

Export Citation Format

Share Document