scholarly journals Multi-objective NSGA-II based community detection using dynamical evolution social network

2021 ◽  
Vol 11 (5) ◽  
pp. 4502
Author(s):  
Muhammed E. Abd Alkhalec Tharwat ◽  
Mohd Farhan Md Fudzee ◽  
Shahreen Kasim ◽  
Azizul Azhar Ramli ◽  
Mohammed K. Ali
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Dynamical Evolution ◽  
Nsga Ii ◽  
Normalized Mutual Information ◽  
Detection Algorithms ◽  
Dynamical Aspect ◽  
The Social ◽  
Almost All ◽  
Single Objective

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

Multiobjective Group Search Optimization Approach for Community Detection in Networks

2016 ◽  
Vol 7 (3) ◽  
pp. 50-70 ◽  
Author(s):  
Nidhi Arora ◽  
Hema Banati
Keyword(s):  
Community Detection ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Multiple Features ◽  
Detection Algorithms ◽  
Search Optimization ◽  
Art Community ◽  
Group Search ◽  
Single Objective ◽  
Connected Communities

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

scholarly journals Towards effective discovery of natural communities in complex networks and implications in e-commerce

2020 ◽  
Author(s):  
Swarup Chattopadhyay ◽  
Tanmay Basu ◽  
Asit K. Das ◽  
Kuntal Ghosh ◽  
Late C. A. Murthy
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Similarity Measure ◽  
Recommender System ◽  
Data Clustering ◽  
Ground Truth ◽  
Normalized Mutual Information ◽  
Detection Algorithms ◽  
Node Similarity ◽  
Art Community

AbstractAutomated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.

scholarly journals Narušavanje pravopisne norme u ranojezičnoj neformalnoj komunikaciji (na primjeru SMS poruka i internetske društvene mreže Facebook)

2013 ◽  
Vol 8 (1.) ◽  
Author(s):  
Slavica Vrsaljko ◽  
Tea Ljubimir
Keyword(s):  
Social Networks ◽  
Elementary School ◽  
Social Network ◽  
Mobile Phones ◽  
Fourth Grade ◽  
The Internet ◽  
Spelling Errors ◽  
The Social ◽  
Fourth Grade Students ◽  
Almost All

SMS messaging and communicating on social networks are increasingly widespread forms of informal communication. Mobile phones have almost all, and in addition they open profiles on the Internet social network, corresponding in this way with their peers. In writing messages is being recorded a large number of spelling errors, most of errors are those whose adoption is foreseen in the the lower grades of elementary school. In order to determine the level of mastery of linguistic norms, the message will be analysed as well as comments from the social networks of fourth-grade students.

scholarly journals A bi-directional approach to comparing the modular structure of networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel Straulino ◽  
Mattie Landman ◽  
Neave O’Clery
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Distance Measure ◽  
Detection Algorithm ◽  
The Other ◽  
Modular Structure ◽  
Quality Function ◽  
Normalized Mutual Information ◽  
Connectivity Structure ◽  
Detection Algorithms

AbstractHere we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network’s connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B’s partition relative to A’s own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.

scholarly journals An efficient method to detect communities in social networks

2021 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W AISHA BANU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with four community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, Leiden algorithm and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

DynComm

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Community Detection ◽  
Dynamic Networks ◽  
R Package ◽  
Research Area ◽  
Evolving Networks ◽  
The Social ◽  
Analysis Of Dynamics ◽  
Support Students

The analysis of dynamics in networks represents a great deal in the Social Network Analysis research area. To support students, teachers, developers, and researchers in this work, we introduce a novel R package, namely DynComm. It is designed to be a multi-language package used for community detection and analysis on dynamic networks. The package introduces interfaces to facilitate further developments and the addition of new and future developed algorithms to deal with community detection in evolving networks. This new package aims to abstract the programmatic interface of the algorithms, whether they are written in R or other languages, and expose them as functions in R.

scholarly journals A Semantic Community Detection Algorithm Based on Quantizing Progress

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xu Han ◽  
Deyun Chen ◽  
Hailu Yang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Semantic Information ◽  
Semantic Space ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Ideal

The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.

scholarly journals Discovering Social Community Structures Based on Human Mobility Traces

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Cong-Binh Nguyen ◽  
Seokhoon Yoon ◽  
Jangyoung Kim
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Community Detection ◽  
Clustering Algorithms ◽  
Social Groups ◽  
Real Life ◽  
Social Connections ◽  
Social Community ◽  
Human Interactions ◽  
The Social

We consider a community detection problem in a social network. A social network is composed of smaller communities; that is, a society can be partitioned into different social groups in which the members of the same group maintain stronger and denser social connections than individuals from different groups. In other words, people in the same community have substantially interdependent social characteristics, indicating that the community structure may facilitate understanding human interactions as well as individual’s behaviors. We detect the social groups within a network of mobile users by analyzing the Bluetooth-based encounter history from a real-life mobility dataset. Our community detection methodology focuses on designing similarity measurements that can reflect the degree of social connections between users by considering tempospatial aspects of human interactions, followed by clustering algorithms. We also present two evaluation methods for the proposed schemes. The first method relies on the natural properties of friendship, where the longevity, frequency, and regularity characteristics of human encounters are considered. The second is a movement-prediction-based method which is used to verify the social ties between users. The evaluation results show that the proposed schemes can achieve high performance in detecting the social community structure.

Sign in / Sign up

Export Citation Format

Share Document