scholarly journals Modularity based Community Detection in Social Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 564-569
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Research Community ◽  
Incorrect Result ◽  
The Real

The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

scholarly journals The robustness of reciprocity: Experimental evidence that each form of reciprocity is robust to the presence of other forms of reciprocity

2020 ◽  
Vol 6 (23) ◽  
pp. eaba0504
Author(s):  
David Melamed ◽  
Brent Simpson ◽  
Jered Abernathy
Keyword(s):  
Social Networks ◽  
Prosocial Behavior ◽  
Experimental Evidence ◽  
Real World ◽  
Strong Support ◽  
Theoretical Models ◽  
Current Understanding ◽  
The Real ◽  
Topological Features

Prosocial behavior is paradoxical because it often entails a cost to one’s own welfare to benefit others. Theoretical models suggest that prosociality is driven by several forms of reciprocity. Although we know a great deal about how each of these forms operates in isolation, they are rarely isolated in the real world. Rather, the topological features of human social networks are such that people are often confronted with multiple types of reciprocity simultaneously. Does our current understanding of human prosociality break down if we account for the fact that the various forms of reciprocity tend to co-occur in nature? Results of a large experiment show that each basis of human reciprocity is remarkably robust to the presence of other bases. This lends strong support to existing models of prosociality and puts theory and research on firmer ground in explaining the high levels of prosociality observed in human social networks.

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

Penguins Search Optimization Algorithm for Community Detection in Complex Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mohamed Guendouz ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Optimization ◽  
Density Evaluation ◽  
Evaluation Measure ◽  
Modularity Density ◽  
Initialization Strategy

In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.

scholarly journals Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 15-32
Author(s):  
Ehsan Ardjmand ◽  
William A. Young II ◽  
Najat E. Almasarwah
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Academic Community ◽  
Detection Methods ◽  
The Unconscious ◽  
Comprehensive Review ◽  
Wide Range

Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

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.

WEIGHTED ACCELERATED GROWTH MODEL OF COMPLEX NETWORKS

2007 ◽  
Vol 21 (23n24) ◽  
pp. 4064-4066
Author(s):  
C. C. LEUNG ◽  
H. F. CHAU
Keyword(s):  
Complex Networks ◽  
Structure Formation ◽  
Growth Model ◽  
Real World ◽  
Organizational Structures ◽  
Weighted Network ◽  
The Real ◽  
Accelerated Growth

We introduce and study a toy model which mimics the structure formation of a typical weighted network in the real world. In particular, the organizational structures of our networks are found to be consistent with real-world networks.

The epidemic network construction and immunization based on node strength

2018 ◽  
Vol 32 (26) ◽  
pp. 1850319 ◽  
Author(s):  
Fuzhong Nian ◽  
Longjing Wang ◽  
Zhongkai Dang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Scale Free Network ◽  
Network Construction ◽  
The Real ◽  
Scale Free ◽  
Social Ability ◽  
The Social ◽  
Free Network

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization.

scholarly journals The Evolution of Price Competition Game on Complex Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Feng Jie Xie ◽  
Jing Shi
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Price Competition ◽  
Small World ◽  
Square Lattice ◽  
Small World Network ◽  
Scale Free Network ◽  
The Real ◽  
Scale Free ◽  
Free Network

The well-known “Bertrand paradox” describes a price competition game in which two competing firms reach an outcome where both charge a price equal to the marginal cost. The fact that the Bertrand paradox often goes against empirical evidences has intrigued many researchers. In this work, we study the game from a new theoretical perspective—an evolutionary game on complex networks. Three classic network models, square lattice, WS small-world network, and BA scale-free network, are used to describe the competitive relations among the firms which are bounded rational. The analysis result shows that full price keeping is one of the evolutionary equilibriums in a well-mixed interaction situation. Detailed experiment results indicate that the price-keeping phenomenon emerges in a square lattice, small-world network and scale-free network much more frequently than in a complete network which represents the well-mixed interaction situation. While the square lattice has little advantage in achieving full price keeping, the small-world network and the scale-free network exhibit a stronger capability in full price keeping than the complete network. This means that a complex competitive relation is a crucial factor for maintaining the price in the real world. Moreover, competition scale, original price, degree of cutting price, and demand sensitivity to price show a significant influence on price evolution on a complex network. The payoff scheme, which describes how each firm’s payoff is calculated in each round game, only influences the price evolution on the scale-free network. These results provide new and important insights for understanding price competition in the real world.

Community cores expansion for overlapping community detection in complex networks

2018 ◽  
Vol 32 (33) ◽  
pp. 1850405 ◽  
Author(s):  
Yongjie Yan ◽  
Guang Yu ◽  
Xiangbin Yan ◽  
Hui Xie
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Local Structure ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Clique Percolation ◽  
Overlapping Community ◽  
Synthetic Datasets

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.

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