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.

scholarly journals An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Shuxia Ren ◽  
Shubo Zhang ◽  
Tao Wu
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Transition Probability ◽  
Clustering Algorithms ◽  
Detection Algorithm ◽  
Community Information ◽  
The Mean ◽  
Community Detection Algorithm ◽  
Spectral Clustering Algorithm

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

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 An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Wang ◽  
Xinzheng Niu ◽  
Jiahui Zhu ◽  
Zuoyan Liu
Keyword(s):  
Community Detection ◽  
Trajectory Analysis ◽  
Moving Objects ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Similarity Matrix ◽  
Trajectory Data ◽  
Trajectory Similarity ◽  
Community Detection Algorithm

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

scholarly journals A Modularity Degree Based Heuristic Community Detection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongming Chen ◽  
Dongqi Wang ◽  
Fangzhao Xia
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Basic Problem ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Resolution Limit ◽  
Measure Function ◽  
Optimization Function ◽  
Community Detection Algorithm

A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

The independence of the centrality for community detection

2018 ◽  
Vol 29 (07) ◽  
pp. 1850060
Author(s):  
Jin Lei ◽  
Wang Xiao Juan ◽  
Zhang Yong
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Local Density ◽  
Clustering Algorithms ◽  
Detection Algorithm ◽  
Community Centers ◽  
Detection Algorithms ◽  
Research Designs ◽  
Simulation Results ◽  
Community Detection Algorithm

Community detection is significative in the complex network. This paper focuses on community detection based on clustering algorithms. We tend to find out the central nodes of the communities by centrality algorithms. Firstly, we define the distance between nodes using similarity. Then, a new centrality measuring the local density of nodes is put forward. Combining the independence of the centrality, the nodes in the network can be divided into four classes. Leveraging the product of centrality and independence, the central nodes in the network are easily identified. We also find that we can distinguish bridge nodes from central nodes using centrality and independence. This research designs a community detection algorithm combining centrality and independence. Simulation results reveal that our centrality is more effective than existing centralities in measuring local density and identifying community centers. Compared with other community detection algorithms, results prove the effectiveness of our algorithm. This paper just shows one application of independence of the centrality. There may be more useful applications of it.

scholarly journals Community detection in networks: a game-theoretic framework

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yan Chen ◽  
Xuanyu Cao ◽  
K. J. Ray Liu
Keyword(s):  
Community Detection ◽  
Real World ◽  
Expectation Maximization ◽  
Detection Method ◽  
Detection Algorithm ◽  
Generative Models ◽  
Detection Methods ◽  
Game Theoretic ◽  
Community Detection Algorithm ◽  
General Community

AbstractReal-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks and thus gain more insights into the complicated networks. Strategic nodes belonging to different communities interact with each other to decide mutual links in the networks. Hitherto, various community detection methods have been proposed in the literature, yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits, i.e., maximizing their own utility functions when forming a community. Given the proposed game-theoretic generative models for communities, we present a general community detection algorithm based on expectation maximization (EM). Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state of the art.

scholarly journals Visibility graph based temporal community detection with applications in biological time series

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minzhang Zheng ◽  
Sergii Domanskyi ◽  
Carlo Piermarocchi ◽  
George I. Mias
Keyword(s):  
Time Series ◽  
Community Detection ◽  
Shortest Path ◽  
Detection Algorithm ◽  
Visibility Graph ◽  
Main Stem ◽  
Detection Methods ◽  
Biological Time ◽  
Dual Perspective ◽  
Community Detection Algorithm

AbstractTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.

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