Fast detection of the fuzzy communities based on leader-driven algorithm

2018 ◽  
Vol 32 (06) ◽  
pp. 1850058
Author(s):  
Changjian Fang ◽  
Dejun Mu ◽  
Zhenghong Deng ◽  
Jun Hu ◽  
Chen-He Yi
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Learning Community ◽  
Network Structure ◽  
Fast Algorithm ◽  
Network Size ◽  
Local Minima ◽  
Large Networks ◽  
Number Of Clusters ◽  
Fast Detection

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

HEXACO personality factors and their associations with Facebook use and Facebook network characteristics

2018 ◽  
Author(s):  
Riana Brown ◽  
Sam G. B. Roberts ◽  
Thomas V. Pollet
Keyword(s):  
Social Networks ◽  
Network Size ◽  
Openness To Experience ◽  
Personality Factors ◽  
Number Of Clusters ◽  
Network Characteristics ◽  
Online Networks ◽  
Facebook Use ◽  
Objectively Measured ◽  
Size And Structure

Personality factors affect the properties of ‘offline’ social networks, but how they are associated with the structural properties of online networks is still unclear. We investigated how the six HEXACO personality factors (Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to Experience) relate to Facebook use and three objectively measured Facebook network characteristics - network size, density, and number of clusters. Participants (n = 107, mean age = 20.6, 66% female) extracted their Facebook networks using the GetNet app, completed the 60-item HEXACO questionnaire and the Facebook Usage Questionnaire. Users high in Openness to Experience spent less time on Facebook. Extraversion was positively associated with network size and the number of network clusters (but not after controlling for size). These findings suggest that personality factors are associated with Facebook use and the size and structure of Facebook networks, and that personality is an important influence on both online and offline sociality.

scholarly journals Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

2020 ◽  
Vol 11 (1) ◽  
pp. 18-24
Author(s):  
Morgan Prust ◽  
Abby Halm ◽  
Simona Nedelcu ◽  
Amber Nieves ◽  
Amar Dhand
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Network Structure ◽  
Network Size ◽  
Personal Network ◽  
Mapping Tool ◽  
Social Network Structure ◽  
Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

scholarly journals Social Network Structure Is Related to Functional Improvement From Home-Based Telerehabilitation After Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Archana Podury ◽  
Sophia M. Raefsky ◽  
Lucy Dodakian ◽  
Liam McCafferty ◽  
Vu Le ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Depressive Symptoms ◽  
Network Structure ◽  
Network Size ◽  
Network Density ◽  
Stroke Patients ◽  
Historical Cohort ◽  
Home Based ◽  
Social Network Structure

Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for stroke patients and evaluated which social factors might be related to motor gains and reduced depressive symptoms.Methods: Stroke patients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and mood. At the 6-week midpoint, we mapped each patient's personal social network and evaluated relationships between social network metrics and functional improvements from TR. Finally, we compared social networks of TR patients with a historical cohort of 176 stroke patients who did not receive any TR to identify social network differences.Results: Both network size and network density were related to walk time improvement (p = 0.025; p = 0.003). Social network density was related to arm motor gains (p = 0.003). Social network size was related to reduced depressive symptoms (p = 0.015). TR patient networks were larger (p = 0.012) and less dense (p = 0.046) than historical stroke control networks.Conclusions: Social network structure is positively related to improvement in motor status and mood from TR. TR patients had larger and more open social networks than stroke patients who did not receive TR. Understanding how social networks intersect with TR outcomes is crucial to maximize effects of virtual rehabilitation.

scholarly journals Toward Curation and Personality-Driven Social Networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Bas Hofstra
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Social Stratification ◽  
Network Formation ◽  
Research Question ◽  
Network Size ◽  
Personal Network ◽  
Online Technologies ◽  
Emergent Technologies ◽  
Stable Network

Do humans have bigger or smaller social networks today? We reflect on the state of this research question and assert that an updated approach is needed to understand the effects of emergent technologies on network structure. Although the absolute changes in average network size are likely to remain elusive, recent perspectives converge on the idea that online technologies make it easier for individuals to shape—or curate—their social connections. Here we merge conflicting views and specify mechanisms through which curation technologies may impact personal network structure. Looking forward, we suggest personality will become more influential in network formation and maintenance when aided by technological levers. Consequently, curation technologies have the potential to increase differences in networks between types of people (for example, extroverts vs introverts) and thus generate new forms of social stratification, despite preserving a stable network size on average. The Comment concludes with empirical and theoretical implications, including the importance of attending to dispersion and examining the societal ramifications of personality-driven curation.

scholarly journals Loneliness is negatively related to Facebook network size, but not related to Facebook network structure

2018 ◽  
Author(s):  
Riana M. Brown ◽  
Sam G. B. Roberts ◽  
Thomas Victor Pollet
Keyword(s):  
Network Structure ◽  
Path Length ◽  
Large Body ◽  
Network Size ◽  
Individual Characteristics ◽  
Average Path Length ◽  
Physical And Mental Health ◽  
Number Of Clusters ◽  
Size Number ◽  
Study Participants

High levels of loneliness are associated with poorer outcomes for physical and mental health and a large body of research has examined how using social media sites such as Facebook are associated with loneliness. Time spent on Facebook tends to be associated with higher levels of loneliness, whereas a larger number of Facebook Friends and more active use of Facebook tends to be associated with lower levels of loneliness. However, whilst the network size and structure of ‘offline’ networks have been associated with loneliness, how the network structure on Facebook is associated with loneliness is still unclear. In this study, participants used the Getnet app to directly extract information on network size (number of Facebook Friends), density, number of clusters in the network, and average path length from their Facebook networks, and completed the 20-item UCLA Loneliness questionnaire. In total, 107 participants (36 men, 71 women, mean age = 20.6, SD = 2.7) took part in the study. Participants with a larger network size reported significantly lower feelings of loneliness. In contrast, network density, number of clusters, and average path length were not significantly related to loneliness. These results suggest that whilst having a larger Facebook network is related to feelings of social connection to others, the structure of the Facebook network may be a less important determinant of loneliness than other factors such as active or passive use of Facebook and individual characteristics of Facebook users.

scholarly journals Loneliness is negatively related to Facebook network size, but not related to Facebook network structure

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Riana M. Brown ◽  
Sam G. B. Roberts ◽  
Thomas V. Pollet
Keyword(s):  
Network Structure ◽  
Path Length ◽  
Large Body ◽  
Network Size ◽  
Individual Characteristics ◽  
Average Path Length ◽  
Physical And Mental Health ◽  
Number Of Clusters ◽  
Size Number ◽  
Study Participants

High levels of loneliness are associated with poorer outcomes for physical and mental health and a large body of research has examined how using social media sites such as Facebook is associated with loneliness. Time spent on Facebook tends to be associated with higher levels of loneliness, whereas a larger number of Facebook Friends and more active use of Facebook tends to be associated with lower levels of loneliness. However, whilst the network size and structure of ‘offline’ networks have been associated with loneliness, how the network structure on Facebook is associated with loneliness is still unclear. In this study, participants used the Getnet app to directly extract information on network size (number of Facebook Friends), density, number of clusters in the network, and average path length from their Facebook networks, and completed the 20-item UCLA Loneliness questionnaire. In total, 107 participants (36 men, 71 women, Mage = 20.6, SDage = 2.7) took part in the study. Participants with a larger network size reported significantly lower feelings of loneliness. In contrast, network density, number of clusters, and average path length were not significantly related to loneliness. These results suggest that whilst having a larger Facebook network is related to feelings of social connection to others, the structure of the Facebook network may be a less important determinant of loneliness than other factors such as active or passive use of Facebook and individual characteristics of Facebook users.

scholarly journals Social network structure and composition in former NFL football players

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amar Dhand ◽  
Liam McCafferty ◽  
Rachel Grashow ◽  
Ian M. Corbin ◽  
Sarah Cohan ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
National Football League ◽  
Treatment Options ◽  
Network Size ◽  
Brain Trauma ◽  
Future Research ◽  
Football Players ◽  
Contact Sports

AbstractSocial networks have broad effects on health and quality of life. Biopsychosocial factors may also modify the effects of brain trauma on clinical and pathological outcomes. However, social network characterization is missing in studies of contact sports athletes. Here, we characterized the personal social networks of former National Football League players compared to non-football US males. In 303 former football players and 269 US males, we found that network structure (e.g., network size) did not differ, but network composition (e.g., proportion of family versus friends) did differ. Football players had more men than women, and more friends than family in their networks compared to US males. Black players had more racially diverse networks than White players and US males. These results are unexpected because brain trauma and chronic illnesses typically cause diminished social relationships. We anticipate our study will inform more multi-dimensional study of, and treatment options for, contact sports athletes. For example, the strong allegiances of former athletes may be harnessed in the form of social network interventions after brain trauma. Because preserving health of contact sports athletes is a major goal, the study of social networks is critical to the design of future research and treatment trials.

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 Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

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