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.

scholarly journals Social Network Community Detection Using Agglomerative Spectral Clustering

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ulzii-Utas Narantsatsralt ◽  
Sanggil Kang
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithms ◽  
Real Life ◽  
Clustering Method ◽  
Network Community ◽  
Improved Performance ◽  
Spectral Clustering Method

Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.

Community Structure Detection Using Firefly Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 52-70 ◽  
Author(s):  
Ameera Saleh Jaradat ◽  
Safa'a Bani Hamad
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Graph Partitioning ◽  
Firefly Algorithm ◽  
Real Life ◽  
People Detection ◽  
Continuous Growth ◽  
Structure Detection ◽  
Partitioning Problem ◽  
And Function

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

Online vrienden bepalen de overtuigingskracht van SNS-campagnes

2011 ◽  
Vol 39 (4) ◽  
Author(s):  
Guda van Noort ◽  
Marjolijn Antheunis ◽  
Eva van Reijmersdal
Keyword(s):  
Social Network ◽  
Social Context ◽  
Crucial Role ◽  
Social Network Sites ◽  
Social Connections ◽  
Social Connection ◽  
Marketing Communication ◽  
The Social

Online Friends Determine the Persuasiveness of SNS Advertising Campaigns Online Friends Determine the Persuasiveness of SNS Advertising Campaigns Marketers more and more design advertising campaigns especially for Social Network Sites (SNS), with the aim that SNS users forward these campaigns to their online network. By means of a survey, this study investigates whether the persuasiveness of such campaigns is determined by the strength of the social connection between receiver and the sender of the campaign. The results support the idea that SNS campaigns are more persuasive when forwarded by close friends, than when forwarded by less strong social connections. Thus, the social context plays a crucial role in the persuasiveness of marketing communication activities within SNS.

Features of behavior of Russian and American students on social network Facebook

2021 ◽  
pp. 119-123
Author(s):  
M.I. Krishtal ◽  
◽  
A.V. Shchekoturov
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Student Behavior ◽  
Social Activism ◽  
Real Life ◽  
The United States ◽  
Public And Private ◽  
The Social ◽  
American Students ◽  
Making Friends

Presented is the analysis of peculiarities of behavior of Russian and American students in the social network Facebook. The focus of the study is on what activities students of the two countries are most often engaged in, as well as on what motives they motivate when adding users to the personal list of friends. The main method of research is a formalized interview (N = 266). Students of two higher educational institutions located in Kaliningrad (Russia) and Philadelphia (USA) were interviewed. In anticipation of the analysis, according to the functionality, the types of activities on Facebook were highlighted, i.e. social, functional and cognitive. Also forms of activity were divided into public and private according to the nature of their manifestation (open or hidden). The motives for making friends were typologized according to the user’s orientation towards the development of “binding” or “connecting” social capital. As a result of the analysis, it was revealed that students from the United States are more likely to engage in public activities on Facebook than students from Russia, which is expressed in more active commenting on posts, publishing content on their personal page and friends’ pages. Russian students prefer private activities (chat and viewing other people’s pages). The functional type of activity, expressed in the use of gaming and non-gaming applications, turned out to be the most unpopular form of pastime on Facebook among students in both countries. It was also found that students at two universities are more focused on the development of “connecting” social capital. At the same time, for Russian students the dating factor in real life does not act as an important motive for adding friends to the list as for American ones. It is suggested that the basis for the differences discovered are the features of the cultural environments in which students live. The Russian environment involves more cautious participation in public life, the American stimulates social activism. Significant gender differences in peculiarities of student behavior in Facebook network could not be identified.

Measuring the Impact of Social Media

2011 ◽  
pp. 24-36 ◽  
Author(s):  
Kimiz Dalkir
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Evaluation Method ◽  
Real Life ◽  
Work Processes ◽  
The Social ◽  
Post Test ◽  
The Impact

This chapter focuses on a method, social network analysis (SNA) that can be used to assess the quantity and quality of connection, communication and collaboration mediated by social tools in an organization. An organization, in the Canadian public sector, is used as a real-life case study to illustrate how SNA can be used in a pre-test/post-test evaluation design to conduct a comparative assessment of methods that can be used before, during and after the implementation of organizational change in work processes. The same evaluation method can be used to assess the impact of introducing new social media such as wikis, expertise locator systems, blogs, Twitter and so on. In other words, while traditional pre-test/post-test designs can be easily applied to social media, the social media tools themselves can be added to the assessment toolkit. Social network analysis in particular is a good candidate to analyze the connections between people and content as well as people with other people.

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

scholarly journals Understanding attribute and social circle correlation in social networks

2019 ◽  
pp. 1228-1242
Author(s):  
PRANAV NERURKAR ◽  
MADHAV CHANDANE ◽  
SUNIL BHIRUD
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Coarse Grained ◽  
Unified Framework ◽  
Community Discovery ◽  
Social Circle ◽  
The Social ◽  
Circle Formation

Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained descriptions from data, consists of two families of techniques, community discovery and clustering. These approaches are infeasible for the purpose of automation of social circle creation as they fail on social networks. A reason for this failure could be lack of knowledge of the global structure of the social network or the sparsity that exists in data from social networking websites. As individuals do in real life, OSN clients dependably attempt to broaden their groups of contacts in order to fulfill different social demands. This means that ‘homophily’ would exist among OSN users and prove useful in the task of social circle detection. Based on this intuition, the current inquiry is focused on understanding ‘homophily’ and its role in the process of social circle formation. Extensive experiments are performed on egocentric networks (ego is user, alters are friends) extracted from prominent OSNs like Facebook, Twitter, and Google+. The results of these experiments are used to propose a unified framework: feature extraction for social circles discovery (FESC). FESC detects social circles by jointly modeling ego-net topology and attributes of alters. The performance of FESC is compared with standard benchmark frameworks using metrics like edit distance, modularity, and running time to highlight its efficacy.

scholarly journals COMMUNITY STRUCTURE IN JAZZ

2003 ◽  
Vol 06 (04) ◽  
pp. 565-573 ◽  
Author(s):  
PABLO M. GLEISER ◽  
LEON DANON
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Quantitative Analysis ◽  
Social Interactions ◽  
Racial Segregation ◽  
Collaboration Network ◽  
Community Size ◽  
The Social ◽  
E Mail ◽  
Different Levels

Using a database of jazz recordings we study the collaboration network of jazz musicians. We define the network at two different levels. First we study the collaboration network between individuals, where two musicians are connected if they have played in the same band. Then we consider the collaboration between bands, where two bands are connected if they have a musician in common. The community structure analysis reveals that these constructions capture essential ingredients of the social interactions between jazz musicians. We observe correlations between recording locations, racial segregation and the community structure. A quantitative analysis of the community size distribution reveals a surprising similarity with an e-mail based social network recently studied.

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.

scholarly journals The social network and communicative complexity: preface to theme issue

2012 ◽  
Vol 367 (1597) ◽  
pp. 1782-1784 ◽  
Author(s):  
Todd M. Freeberg ◽  
Terry J. Ord ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Animal Species ◽  
Social Complexity ◽  
Social Groups ◽  
Social Worlds ◽  
Theme Issue ◽  
The Social ◽  
Signalling Systems

The complex social worlds of many animal species may be linked to complex communicative systems in those species. We now have evidence in diverse taxa and in different communicative modalities suggesting that complexity in social groups can drive complexity in signalling systems. The aim of this theme issue is to develop the theory behind this link between social complexity and communicative complexity, and to provide an overview of the lines of research testing this link.

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