Quantitative Function for Community Detection

2012 ◽  
Vol 433-440 ◽  
pp. 6441-6446 ◽  
Author(s):  
Jin Xia Liu ◽  
Jian Chao Zeng ◽  
Yao Wen Xue ◽  
Ying Wang
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Numerical Results ◽  
C Value ◽  
Modularity Density ◽  
Quality Indexes ◽  
Classical Optimization

Detecting and characterizing the community structure of complex network is fundamental. We compare the classical optimization indexes of modularity and modularity density, which are quality indexes for a partition of a network into communities. Based on this, we propose a quantitative function for community partition, named communitarity or C value. We demonstrate that the quantitative is superior to modularity Q and modularity density D. Both theoretical and numerical results show that optimizing the new index not only can resolve small modules, but also can correctly identify the number of communities.

scholarly journals LIMITED RESOLUTION AND MULTIRESOLUTION METHODS IN COMPLEX NETWORK COMMUNITY DETECTION

2007 ◽  
Vol 07 (03) ◽  
pp. L209-L214 ◽  
Author(s):  
JUSSI M. KUMPULA ◽  
JARI SARAMÄKI ◽  
KIMMO KASKI ◽  
JÁNOS KERTÉSZ
Keyword(s):  
Simulated Annealing ◽  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Tuning Parameter ◽  
Challenging Problem ◽  
Resolution Limit ◽  
Network Community ◽  
Multiresolution Methods

Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernández, and Gómez. These methods are studied analytically, and applied to three test networks using simulated annealing.

Active semisupervised community detection based on asymmetric similarity measure

2015 ◽  
Vol 29 (13) ◽  
pp. 1550078 ◽  
Author(s):  
Mingwei Leng ◽  
Liang Huang ◽  
Longjie Li ◽  
Hanhai Zhou ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Community Structure ◽  
Prior Knowledge ◽  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Directed Network ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Similarity Method ◽  
Asymmetric Similarity

Semisupervised community detection algorithms use prior knowledge to improve the performance of discovering the community structure of a complex network. However, getting those prior knowledge is quite expensive and time consuming in many real-world applications. This paper proposes an active semisupervised community detection algorithm based on the similarities between nodes. First, it transforms a given complex network into a weighted directed network based on the proposed asymmetric similarity method, some informative nodes are selected to be the labeled nodes by using an active mechanism. Second, the proposed algorithm discovers the community structure of a complex network by propagating the community labels of labeled nodes to their neighbors based on the similarity between a node and a community. Finally, the performance of the proposed algorithm is evaluated with three real networks and one synthetic network and the experimental results show that the proposed method has a better performance compared with some other community detection algorithms.

scholarly journals Multiobjective Brain Storm Optimization Community Detection Method Based on Novelty Search

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoying Pan ◽  
Jia Wang ◽  
Miao Wei ◽  
Hongye Li
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Detection Method ◽  
Premature Convergence ◽  
Practical Significance ◽  
Network Community ◽  
Brain Storm Optimization ◽  
Optimal Network ◽  
Novelty Search

A complex network is characterized by community structure, so it is of great theoretical and practical significance to discover hidden functions by detecting the community structure in complex networks. In this paper, a multiobjective brain storm optimization based on novelty search (MOBSO-NS) community detection method is proposed to solve the current issue of premature convergence caused by the loss of diversity in complex network community detection based on multiobjective optimization algorithm and improve the accuracy of community discovery. The proposed method designs a novel search strategy where novelty individuals are first constructed to improve the global search ability, thus avoiding falling into local optimal solutions; then, the objective space is divided into 3 clusters: elite cluster, ordinary cluster, and novel cluster, which are mapped to the decision space, and finally, the populations are disrupted and merged. In addition, the introduction of a restarting strategy is introduced to avoid stagnation by premature convergence. Experimental results show that the algorithm with good global searchability can find the Pareto optimal network community structure set with uniform distribution and high convergence and excavate the network community with higher quality.

scholarly journals Community Detection Based on Differential Evolution Using Modularity Density

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 218 ◽  
Author(s):  
Caihong Liu ◽  
Qiang Liu
Keyword(s):  
Community Structure ◽  
Differential Evolution ◽  
Community Detection ◽  
Differential Evolution Algorithm ◽  
Limit Problem ◽  
Detection Methods ◽  
Tuning Parameter ◽  
Resolution Limit ◽  
Science Field ◽  
Modularity Density

Currently, many community detection methods are proposed in the network science field. However, most contemporary methods only employ modularity to detect communities, which may not be adequate to represent the real community structure of networks for its resolution limit problem. In order to resolve this problem, we put forward a new community detection approach based on a differential evolution algorithm (CDDEA), taking into account modularity density as an optimized function. In the CDDEA, a new tuning parameter is used to recognize different communities. The experimental results on synthetic and real-world networks show that the proposed algorithm provides an effective method in discovering community structure in complex 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.

scholarly journals Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Spatial Dimension ◽  
Economic Dimension ◽  
Complex Network Analysis ◽  
Time Period ◽  
Time Patterns ◽  
Spatio Temporal

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.

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