From sparse to dense and from assortative to disassortative in online social networks.

Sci Rep

1] Beijing-Hong Kong-Singapore Joint Centre for Nonlinear & Complex Systems (Singapore), National University of Singapore, Kent Ridge, 119260, Singapore [2] Department of Physics, National University of Singapore, 117542, Singapore [3] Yale-NUS College, Singapore, 138614, Singapore.

Published: May 2014

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4010929PMC
http://dx.doi.org/10.1038/srep04861DOI Listing

Publication Analysis

Top Keywords

online social
16
social networks
16
sparse dense
8
assortative disassortative
8
empirical online
8
dense assortative
4
online
4
disassortative online
4
social
4
networks
4

Similar Publications

Background: Comprehensive health-related quality of life (QOL) assessment under severe respondent burden constraints requires improved single-item scales for frequently surveyed domains. This article documents how new single-item-per-domain (SIPD) QOL General (QGEN-8) measures were constructed for domains common to SF-36 and results from the first psychometric tests comparing scores for the new measure in relation to those for the SF-36 profile and summary components.

Research Design: Online NORC surveys of adults, ages 19-93 (mean=52 y) representing the US population in 2020 (N=1648) included QGEN-8 and SF-36 items measuring physical (PF), social (SF), role physical (RP) and role emotional (RE) functioning and feelings of bodily pain (BP), vitality (VT), and mental health (MH).

View Article and Find Full Text PDF

While the incidence of Human Immunodeficiency Virus (HIV) infection is decreasing in most age groups worldwide, it is rising among adolescents and young adults, who also face a higher rate of HIV-related deaths. This tech-savvy demographic may benefit from an online patient portal designed to enhance patient activation-empowering them to manage their health independently. However, the effectiveness of such digital health interventions on young HIV patients in Kenya remains uncertain.

View Article and Find Full Text PDF

The COVID-19 pandemic created unprecedented challenges for social connectivity and mental health, especially during mandated shelter-in-place periods. For patients engaged in mental health treatment, the social impact of their shelter-in-place experience remains an area of active investigation. This is particularly relevant in the context of social prescribing, a growing area of clinical intervention where healthcare providers actively refer patients to local social resources or activities to enhance mental health and wellbeing.

View Article and Find Full Text PDF

Background: As the pace of economic development slows, college students are facing an increasingly challenging employment landscape. For instance, the expansion of higher education has led to a swell in the number of job seekers, which has in turn intensified competition. Given the limited job opportunities, it's understandable that many college students are developing a pessimistic employment mindset.

View Article and Find Full Text PDF

A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N = 1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!