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 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.

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.

Determining average path length and average trapping time on generalized dual dendrimer

2015 ◽  
Vol 29 (12) ◽  
pp. 1550072 ◽  
Author(s):  
Ling Li ◽  
Jihong Guan
Keyword(s):  
Path Length ◽  
Small World ◽  
Network Size ◽  
Average Path Length ◽  
Trapping Efficiency ◽  
Trapping Time ◽  
Coordination Numbers ◽  
Central Node ◽  
Trapping Problem ◽  
Structure Properties

Dendrimer has wide number of important applications in various fields. In some cases during transport or diffusion process, it transforms into its dual structure named Husimi cactus. In this paper, we study the structure properties and trapping problem on a family of generalized dual dendrimer with arbitrary coordination numbers. We first calculate exactly the average path length (APL) of the networks. The APL increases logarithmically with the network size, indicating that the networks exhibit a small-world effect. Then we determine the average trapping time (ATT) of the trapping process in two cases, i.e., the trap placed on a central node and the trap is uniformly distributed in all the nodes of the network. In both case, we obtain explicit solutions of ATT and show how they vary with the networks size. Besides, we also discuss the influence of the coordination number on trapping efficiency.

scholarly journals Fat-Burning or Fat-Shaming? Theme and Community Overlap Between Online, Weight-Stigmatizing and Exercise-Promoting Networks.

2021 ◽  
Author(s):  
Ahuitz Rojas-Sánchez ◽  
Jenine K. Harris ◽  
Philippe Sarrazin ◽  
Aïna Chalabaev
Keyword(s):  
Social Media ◽  
Weight Loss ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Structure ◽  
Path Length ◽  
Average Path Length ◽  
Common Word ◽  
Total Degree

Abstract Purpose: This study aimed to determine if networks of users consistently posting about exercise and fat exist and overlap on social media sites.Method: We collected 3,772,507 posts from Twitter that included the words “fat” and “exercise”. Using network structure methods, we identified communities of interconnected users and overlaps between those tweeting “fat” and those tweeting “exercise”. Results: Common word pairings were identified using Natural Language Processing (NLP). Networks of users consistently talking about exercise (n=3,573) and fat (n=2,007) were found on Twitter. An increased mean total-degree and reduced average path length indicate that the fitness-talk network serves as a connecting bridge between highly scattered communities of the weight-talk network. Conclusion: We identified groups on Twitter dedicated to consistently producing weight stigmatizing content and promoting exercise with weight-loss messages. These groups partially overlap with pro-health groups which could lead to users looking for exercise advice in Twitter to find themselves immersed in a stigmatizing network.

C2, Networks, and Self-Synchronization

2014 ◽  
pp. 191-215 ◽  
Author(s):  
A. H. Dekker
Keyword(s):  
Experimental Data ◽  
Network Topology ◽  
Network Structure ◽  
Network Theory ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Scale Free ◽  
Node Connectivity ◽  
Network Properties

This chapter examines the connection between network theory and C2, particularly as it relates to self-synchronization, which requires a rich network structure. The richness of the network can be measured by the average degree, the average path length, and the average node connectivity. The chapter explores the connection between these measures and the speed of self-synchronization, together with other network properties, which can affect self-synchronization, resilience, and responsiveness. Two important network structures (random and scale-free) are described in the context of self-synchronization. Experimental data relating network topology to self-synchronization speed is also explored. In particular, the chapter notes the connection between average path length and self-synchronization speed, as well as the importance of good networking between sub-networks.

scholarly journals The Influence of Alliance Innovation Network Structure upon Enterprise Innovation: A Case Study of China’s Energy-Saving and Environment-protection Industry

2018 ◽  
Vol 13 (2) ◽  
pp. 208
Author(s):  
Meng Xu
Keyword(s):  
Energy Saving ◽  
Network Structure ◽  
Betweenness Centrality ◽  
Path Length ◽  
Clique Number ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Environment Protection ◽  
Different Types ◽  
Enterprise Innovation

The Energy-saving and environment-protection industry, an important strategic and emerging industry in China, will develop into a pillar industry. In view of global climate change, environmental pollution, resource depletion and the defects and deficiencies in traditional technology, technology and product innovation constitute the lifeline of energy-saving and environment-protection industry. The alliance network of enterprises will influence, stimulate, and regulate enterprise innovation greatly. A comprehensive analysis of alliance data of China's energy-saving and environment-protection industry from 2000 to 2013 by using Ucinet software can reveal the network structure parameters such as degree, clique number, average path length, clustering coefficient, and betweenness centrality, which reflects different types of enterprise networks and different positions of enterprises in different types of networks. A negative regression analysis of enterprise patent data and network structure parameters by using Stata software can make some conclusions that the influences of network characteristics on enterprise innovation reach the maximum in the second year of the window period end, that innovation accumulation, clustering coefficient, betweenness centrality are related to the enterprise innovation, that clique number, network density are negatively related to the enterprise innovation, and that there is an inverted U relationship between average path length and enterprise innovation. It is suggested to increase the accumulation level of innovation, appropriately control the network density, reduce the average path length, improve the betweenness centrality and clustering coefficient of enterprises, so as to improve the overall innovation level. 

scholarly journals Network structure optimization for social networks by minimizing the average path length

2021 ◽  
Author(s):  
Wei Du ◽  
Gang Li ◽  
Xiaochen He
Keyword(s):  
Social Sciences ◽  
Network Structure ◽  
Path Length ◽  
Memetic Algorithm ◽  
Average Path Length ◽  
Major Research ◽  
Research Focus ◽  
Network Diameter ◽  
Network Structure Optimization ◽  
Definition Of

Abstract Network structure plays an important role in the natural and social sciences. Optimization of network structure in achieving specified goals has been a major research focus. In this paper, we propose a definition of structural optimization in terms of minimizing the network’s average path length (APL) by adding edges. We suggest a memetic algorithm to find the minimum-APL solution by adding edges. Experiments show that the proposed algorithm can solve this problem efficiently. Further, we find that APL will ultimately decrease linearly in the process of adding edges, which is affected by the network diameter.

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.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

16Up: Outline of a Study Investigating Wellbeing and Information and Communication Technology Use in Adolescent Twins

2020 ◽  
Vol 23 (6) ◽  
pp. 345-357
Author(s):  
Brittany L. Mitchell ◽  
Katherine M. Kirk ◽  
Kerrie McAloney ◽  
Margaret J. Wright ◽  
Tracey A. Davenport ◽  
...  
Keyword(s):  
Mental Health ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Technology Use ◽  
Health Resources ◽  
Physical And Mental Health ◽  
Ict Use ◽  
Hair Samples ◽  
Information And Communication ◽  
Study Participants

AbstractThe ‘16Up’ study conducted at the QIMR Berghofer Medical Research Institute from January 2014 to December 2018 aimed to examine the physical and mental health of young Australian twins aged 16−18 years (N = 876; 371 twin pairs and 18 triplet sets). Measurements included online questionnaires covering physical and mental health as well as information and communication technology (ICT) use, actigraphy, sleep diaries and hair samples to determine cortisol concentrations. Study participants generally rated themselves as being in good physical (79%) and mental (73%) health and reported lower rates of psychological distress and exposure to alcohol, tobacco products or other substances than previously reported for this age group in the Australian population. Daily or near-daily online activity was almost universal among study participants, with no differences noted between males and females in terms of frequency or duration of internet access. Patterns of ICT use in this sample indicated that the respondents were more likely to use online information sources for researching physical health issues than for mental health or substance use issues, and that they generally reported partial levels of satisfaction with the mental health information they found online. This suggests that internet-based mental health resources can be readily accessed by adolescent Australians, and their computer literacy augurs well for future access to online health resources. In combination with other data collected as part of the ongoing Brisbane Longitudinal Twin Study, the 16Up project provides a valuable resource for the longitudinal investigation of genetic and environmental contributions to phenotypic variation in a variety of human traits.

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