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.

scholarly journals Overlapping community detection algorithm based on similarity of node relationship

2021 ◽  
Author(s):  
Hongtao Liu ◽  
Ning Wang
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Adaptive Optimization ◽  
Community Discovery ◽  
Overlapping Community Detection ◽  
Local Extension ◽  
Optimization Function ◽  
Overlapping Community ◽  
Community Detection Algorithm

Abstract Community discovery is a vital link in the research of social networks. Aiming at the shortcomings of the current local extension-based community discovery algorithm in local community discovery and extension, we propose a based on relationship similarity and local extension Overlapping community detection algorithm(RSLO). First, use the node's relationship similarity strategy to find close seed communities. Then, according to the discovered seed community, the similarity between the neighbor nodes of the community and the community is calculated, and the nodes whose similarity meets the threshold are selected. After that, an adaptive optimization function is used to expand the community. Finally, the free nodes that have not been divided into the community are divided into communities, thereby achieving a more comprehensive community discovery. We conduct experiments on classic datasets and artificially generated networks. The results show that the RSLO algorithm can find accurate and objective community structures.

scholarly journals Modularity-Based Incremental Label Propagation Algorithm for Community Detection

2020 ◽  
Vol 10 (12) ◽  
pp. 4060
Author(s):  
Yunlong Ma ◽  
Yukai Zhao ◽  
Jingwei Wang ◽  
Min Liu ◽  
Weiming Shen ◽  
...  
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Experimental Results ◽  
Optimal Partition ◽  
Propagation Algorithm ◽  
Optimization Function ◽  
Stable Performance ◽  
Community Detection Algorithm ◽  
Fine Tune

Label Propagation Algorithm (LPA) is a fast community detection algorithm. However, since each node is randomly assigned a different label at first, there is serious randomness in the label updating process of LPA, resulting in great instability of detection results. This paper proposes a modularity-based incremental LPA (MILPA) to address this problem. Unlike LPA, MILPA first assigns all nodes the same label, and then repeatedly uses divide strategy to split locally dense connected nodes into a community and give them a new label. After that, MILPA uses modularity gain as the optimization function to fine-tune the label of nodes so as to obtain an optimal partition. The proposed MILPA has been compared with LPA and other known methods. Experimental results show that MILPA has the best and most stable performance in LFR benchmark networks and is comparable to the best algorithm in many real networks.

Complex network structure extraction based on community relevance

2020 ◽  
Vol 31 (04) ◽  
pp. 2050062
Author(s):  
Jingyi Ding ◽  
Licheng Jiao ◽  
Jianshe Wu ◽  
Fang Liu
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Network Structure ◽  
Link Prediction ◽  
Detection Algorithm ◽  
Node Degree ◽  
Network Function ◽  
Detection Algorithms ◽  
New Perspective ◽  
Community Detection Algorithm

One way to understand the network function and analyze the network structure is to find the communities of the network accurately. Now, there are many works about designing algorithms for community detection. Most community detection algorithms are based on modularity optimization. However, these methods not only have disadvantages in computational complexity, but also have the problem of resolution restriction. Designing a community detection algorithm that is fast and effective remains a challenge in the field. We attempt to solve the community detection problem in a new perspective in this paper, believing that the assumption used to solve the link prediction problem is useful for the problem of community detection. By using the similarity between modules of the network, we propose a new method to extract the community structure in this paper. Our algorithm consists of three steps. First, we initialize a community partition based on the distribution of the node degree; second, we calculate the similarity between different communities, where the similarity is the index to describe the closeness of the different communities. We assume that the much closer the two different communities are, the greater the likelihood of being divided together; finally, merge the pairs of communities which has the highest similarity value as possible as we can and stop when the condition is not satisfied. Because the convergence of our algorithm is very fast in the process of merging, we find that our method has advantages both in the computational complexity and in the accuracy when compared with other six classical algorithms. Moreover, we design a new measure to describe how difficulty the network division is.

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 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 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 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.

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.

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