Dynamic Social Networks Research Articles

Privacy Issue: From Static to Dynamic Online Social Networks

Today's societies have become more dependent on social networks in terms of communications and interactions. These networks contain most of the people's activities, which can be public or even personal events. In the last decade, social networks have turned into more prominent platforms in managing and organizing public events. The Egyptian revolution in 2011 and the Ukrainian revolution in 2014 are good reflections of such events. However, it is not known how much the privacy issue of users is revealed in the reality as a consequence of their online interactions. In this work, we investigate the privacy issue in online social networks and its reflection on real life. Our dataset was extracted from the Facebook groups/pages that were involved in the 2019 Iraqi October revolution. Our approach generates a static network using the collected dataset. Then, we investigate the generated static network in terms of detecting potential anomalies. After that, we project the static network (including its characteristics) into a dynamic environment and generate a dynamic network for investigating the privacy issue in the real life. The contribution of this work lies in projecting a real-world static network into a dynamic environment aiming at investigating users' privacy in the real world. Finally, this kind of approach has not been given enough attention in the literature and it is therefore deeply investigated in this article.

Friends' Influence on Academic Performance Among Early Adolescents: The Role of Social Status

Educational research has shown that academic achievement and peer relationships are associated from early school years, where friends play a significant role in influencing students' school adjustment, attitudes, and behaviors. The present study examines how individual academic performance is associated with friendships among 240 5th and 6th graders. The information on students' friendships, academic performance, gender, popularity, and social preference was collected in a convenience sample from 8 classrooms of 2 private-subsidized schools in Santiago, Chile. Longitudinal social network analysis (RSiena) was used to study the co-evolution of academic performance and friendship dynamics, by assessing simultaneously selection and influence processes, and by incorporating social status covariates (popularity and social preference) as moderators of friendship selection and influence. Results showed that friendships were more likely to occur between same-sex peers and between students with similar social status. Regarding social influence, friends influenced individual's academic performance. Moreover, socially preferred students were more likely to be influenced by friends' academic performance, but the same did not occur for popular students. These results might suggest that socially preferred students' attributes (cooperation, reciprocity, and high-quality friendships) would facilitate the influence of academic performance. In contrast, popular students would be less sensitive to their friends' academic performance focusing instead on salient behaviors (e.g., aggression). The findings underline the importance of understanding social network dynamics in educational settings.

A Pipeline for Graph-Based Monitoring of the Changes in the Information Space of Russian Social Media during the Lockdown

With the COVID-19 outbreak and the subsequent lockdown, social media became a vital communication tool. The sudden outburst of online activity influenced information spread and consumption patterns. It increases the relevance of studying the dynamics of social networks and developing data processing pipelines that allow a comprehensive analysis of social media data in the temporal dimension. This paper scopes the weekly dynamics of the information space represented by Russian social media (Twitter and LiveJournal) during a critical period (massive COVID-19 outbreak and first governmental measures). The approach is twofold: 1) build the time series of topic similarity indicators by identifying COVID-related topics in each week and measuring user contribution to the topic space, and 2) cluster user activity and display user-topic relationships on graphs in a dashboard application. The paper describes the development of the pipeline, explains the choices made and provides a case study of the adaptation to virus control measures. The results confirm that social processes and behavior in response to pandemic-triggered changes can be successfully traced in social media. Moreover, the adaptation trends revealed by psychological and sociological studies are reflected in our data and can be explored using the proposed method.

A group-based FMEA approach with dynamic heterogeneous social network consensus reaching model for uncertain reliability assessment

Failure mode and effect analysis (FMEA) is a systematic and multidisciplinary team-based reliability analysis technology used in various industries, which can alleviate the failure risk of products, systems and services. The experts are usually drawn from different departments or areas. Thus, it is important to promote the consensus of experts to produce a collective solution with a high degree of acceptability for reliability assessment of FMEA. Meanwhile, there may be social influence relationships between experts, i.e., their opinions can interact and evolve. Hence, this study first integrates group consensus-reaching mechanism into the FMEA approach. Then, according to the uncertainty and similarity of expert assessments of each risk factor of FMEA, heterogeneous social networks among experts are constructed for opinion interaction, which exactly describes the professional expertise and influence of experts. Considering the important role of opinion dynamics and social network dissemination, dynamic heterogeneous social network consensus reaching model (DHSNCRM) with the minimum adjustment distance is further designed to help experts reach a consensus and obtain the consentient priority of failure modes by using a two-stage feedback process. Finally, a case study of reliability management of the intelligent minimally invasive medical equipment system is presented to verify the effectiveness of our proposed FMEA method.

The impact of social network dynamics on engagement in drug use reduction programs among men and women who use drugs

BackgroundCross-sectional studies have shown strong relationships between social network characteristics and substance use disorder (SUD) treatment engagement. The current study examined associations between longitudinal changes in egocentric social networks of male and female people who use drugs (PWUD) and engagement in drug use reduction programs, broadly defined as either formal SUD treatment or self-help groups. MethodUsing data from an HIV prevention and care study in Baltimore, MD, this study categorized PWUD into those who engaged and did not engage in any drug use reduction programs over two follow-ups during a one-year observation window. The study used multivariate logistic generalized estimating equations (GEE) to examine associations between network composition and stability measures and drug use reduction program engagement, stratified by gender. ResultsOf the 176 subjects participating in drug use reduction programs at baseline, 56.3% remained engaged at one year. Among both male and female respondents, higher turnover into non-kin networks was associated with increased odds of engagement in drug use reduction programs (AOR 1.4; 95% CI: 1.1–1.9, AOR 1.3; 95% CI: 1.0–1.8, respectively). For males, retention of intimate partner networks was associated with increased odds of program engagement (AOR 2.9; 95% CI: 1.1–7.6); for females, higher turnover into kin networks was associated with decreased odds of engagement (AOR 0.8; 95% CI: 0.5–1.0). ConclusionEvaluation of associations between social network characteristics and drug use reduction program engagement appears to benefit from longitudinal analyses that are stratified by gender. Efforts to improve retention in formal SUD treatment or self-help groups might consider intervening through social networks, perhaps by increasing overall levels of social support.

Using Flies to Understand Social Networks.

Many animals live in groups and interact with each other, creating an organized collective structure. Social network analysis (SNA) is a statistical tool that aids in revealing and understanding the organized patterns of shared social connections between individuals in groups. Surprisingly, the application of SNA revealed that Drosophila melanogaster, previously considered a solitary organism, displays group dynamics and that the structure of group life is inherited. Although the number of studies investigating Drosophila social networks is currently limited, they address a wide array of questions that have only begun to capture the details of group level behavior in this insect. Here, we aim to review these studies, comparing their respective scopes and the methods used, to draw parallels between them and the broader body of knowledge available. For example, we highlight how despite methodological differences, there are similarities across studies investigating the effects of social isolation on social network dynamics. Finally, this review aims to generate hypotheses and predictions that inspire future research in the emerging field of Drosophila social networks.

Derivation and characteristics of closed‐form solutions of the fundamental equations for online user dynamics

SummaryThe oscillation model, based on the wave equation on networks, can describe user dynamics in online social networks. The fundamental equation of user dynamics can be introduced into the oscillation model to explicitly describe the causal relation of user dynamics yielded by certain specific network structures. Moreover, by considering the sparseness of the link structure of online social networks, a novel fundamental equation of different forms has been devised. In this article, we derive a closed‐form solution of the new fundamental equation. Also, we show that the closed‐form solution of the new fundamental equation can generate the general solution of the original wave equation and investigate the characteristics of the derived general solution.

Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network

Social influence analysis is a very popular research direction. This article analyzes the social network of musicians and the many influencing factors when musicians create music to rank the influence of musicians. In order to achieve the practical purpose of the model making accurate predictions in the broad music market, the algorithm adopts a macromodel and considers the social network topology network. The article adds the time decay function and the weight of genre influence to the traditional PageRank algorithm, and thus, the MRGT (Musician Ranking based on Genre and Time) algorithm appears. Considering the timeliness of social networks and the continuous development of music, we realized the importance of evolving MRGT into a dynamic social network. Therefore, we adopted audio data analysis technology and used Gaussian distance to classify and study the evolution of music properties at different times and different genres and finally formed the dynamic influence ranking algorithm based on musicians’ social and personal information networks. As a macromodel heuristic algorithm, our model is explanatory, can handle batch data and can avoid unfavorable factors, so as to provide fast speed and improved accuracy. The network can obtain an era indicator DMI (Dynamic Music Influence) that measures the degree of music revolution. DMI is the indicator we provide for music companies to invest in musicians.

Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods.

A model for the co-evolution of dynamic social networks and infectious disease dynamics

Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations of a specific model case show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes; (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not; (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.

Multidimensional Model of Opinion Dynamics in Social Networks: Polarization Indices

Multidimensional Model of Opinion Dynamics in Social Networks: Polarization Indices

Combining epidemiological and ecological methods to quantify social effects on Escherichia coli transmission.

Enteric microparasites like Escherichia coli use multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships and direct physical contact between individuals is paramount for understanding how microparasites like E. coli spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal E. coli transmission experiments in two cattle (Bos taurus) herds, wherein all individuals were equipped with real-time location tracking devices. Following transmission experiments in this model system, we derived temporally dynamic social and contact networks from location data. Estimated social affiliations and dyadic contact frequencies during transmission experiments informed pairwise accelerated failure time models that we used to quantify effects of these sociobehavioural variables on weekly E. coli colonization risk in these populations. We found that sociobehavioural variables alone were ultimately poor predictors of E. coli colonization in feedlot cattle, but can have significant effects on colonization hazard rates (p ≤ 0.05). We show, however, that observed effects were not consistent between similar populations. This work demonstrates that transmission experiments can be combined with real-time location data collection and processing procedures to create an effective framework for quantifying sociobehavioural effects on microparasite transmission.

Analysing (In)formal Relations and Networks in Security Force Assistance: The Case of Niger

ABSTRACT Studies on Security Force Assistance (SFA) have hitherto been dominated by different iterations of the principal-agent perspective to explain relations between provider and recipient. Yet, while such frameworks aptly illustrate these dynamics from a macro perspective, they are inadequate when analysing the complexity of practices on the ground. To mitigate this short-coming, the present article uses a Social Network Analysis framework to provide an in-depth micro analysis of relations between different SFA providers and the recipient state: Niger, focusing on the Belgian Special Forces. Drawing on field observations and more than 40 interviews in Niger, the present study increases our understanding of how dynamic (in)formal social networks impact the development of SFA. It points to the importance of timing, contingency and individual encounters as central in the understanding of how SFA develops and at times strays from strategies.

Identifying Bullies with a Computer Game

Current computer involvement in adolescent social networks (youth between the ages of 11 and 17) provides new opportunities to study group dynamics, interactions amongst peers, and individual preferences. Nevertheless, most of the research in this area focuses on efficiently retrieving information that is explicit in large social networks (e.g., properties of the graph structure), but not on how to use the dynamics of the virtual social network to discover latent characteristics of the real-world social network. In this paper, we present the analysis of a game designed to take advantage of the familiarity of adolescents with online social networks, and describe how the data generated by the game can be used to identify bullies in 5th grade classrooms. We present a probabilistic model of the game and using the in-game interactions of the players (i.e., content of chat messages) infer their social role within their classroom (either a bully or non-bully). The evaluation of our model is done by using previously collected data from psychological surveys on the same 5th grade population and by comparing the performance of the new model with off-the-shelf classifiers.

A study on entrepreneurial opportunities in digital health-care post-Covid-19 from the perspective of developing countries

PurposeThe Covid-19 pandemic encourages organized structures across health sectors to react to the outbreak and digital health technologies have been identified as promising pillars to respond to the outbreak across the globe. The purpose of this study is to address the scoping review aimed at identifying the innovative entrepreneurial opportunities in digital health-care ecosystems. The study highlights the barriers to digital health in developing countries. The study is a forum for the introduction of entrepreneurship prospects in the Indian sense through other foreign readers in developing countries.Design/methodology/approachThis archival study highlights topical literature to explore successful ways of improving market potential in developed countries with respect to digital health. The research offers insights into how a digital health environment could be applied, provides a trajectory that concentrates on key skills and a creative approach.FindingsHealth service providers require developing their competency and skills to accelerate and enhance their entrepreneurial opportunities. Social network dynamics analyzes are particularly helpful in modeling pandemic trends and in tracking post-pandemic digital health needs. The acceptability of digital solutions has to be confronted across developing countries especially in rural areas and a collective public health benefit in form of entrepreneurship to serve the society.Research limitations/implicationsThe research temporarily indicates a creative solution to entrepreneurship in developed countries that can be applied in today’s digital arena.Originality/valueTo raise public understanding of the advantages of digital health systems in developed regions, health education and promotion campaigns should be focused. The idea analysis can be useful benchmarks for policymakers rather than digital health jurisdictions. The post-pandemic provides developed countries a great opportunity for advanced technology to expand connectivity and networking to digital health entrepreneurship.

Robust mean field social control problems with applications in analysis of opinion dynamics

This paper investigates the social optimality of linear quadratic mean field control systems with unmodelled dynamics. The objective of agents is to optimise the social cost, which is the sum of costs of all agents. By variational analysis and direct decoupling methods, the social optimal control problem is analysed, and two equivalent auxiliary robust optimal control problems are obtained for a representative agent. By solving the auxiliary problem with consistent mean field approximations, a set of decentralised strategies is designed, and its asymptotic social optimality is further proved. Next, the results are applied into the study of opinion dynamics in social networks. The evolution of opinions is analysed over finite and infinite horizons, respectively. All opinions are shown to reach agreement with the average opinion in a probabilistic sense. Finally, local interactions among multiple sub-populations are examined via graphon theory.

Crowd attraction-driven community evolution on social network

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

Rural influences on the social network dynamics of district nursing services: A qualitative meta-synthesis.

Background and AimsAs demands on healthcare services grow, fiscal restrictions place increased emphasis on services outside of traditional healthcare settings. Previous research into long‐term‐conditions suggests that social network members (including weaker ties such as acquaintances, community groups, and healthcare professionals) play a key role in illness management. There is limited knowledge about the engagement of social networks in supporting people who are receiving medical interventions at home. This qualitative metasynthesis explores the work and the interactions between district nurses (DN) and informal network members supporting people who are receiving medical interventions at home and living in rural areas.MethodsA search was undertaken on CINAHL, Medline, and PsychINFO for qualitative research articles from 2009 to 2019. Studies that examined DN in rural locations and/or social network support in rural locations were eligible. Fourteen articles were selected.ResultsThematic analysis of results and discussion data from the studies resulted in four themes being developed: the development of both transactional and friend‐like nurse‐patient ties in rural localities, engagement of the wider network in the delivery of good care, blurring of professional boundaries in close community relationships, and issues accessing and navigating formal and informal support in the context of diminishing resources in rural areas.These findings suggest that DNs in rural localities work beyond professional specialties and experience to provide emotional support, help with daily tasks, and build links to communities. There was also evidence that nurses embedded within rural localities developed friend‐like relationships with patients, and negotiated with existing support networks and communities to find support for the patient.ConclusionsFindings indicated that developing strong links with patients and members of their networks does not automatically translate into positive outcomes for patients, and can be unsustainable, burdensome, and disruptive. DNs developing weak ties with patients and building awareness of the structure of individual networks and local sources of support offers avenues for sustainable and tailored community‐based nursing support.

Who has set whose agenda on social media? A dynamic social network analysis of Tweets on Paris attack

ABSTRACT This study investigates the agenda-setting theory in the context of social media through dynamic social network analyses of 102,145 Tweets in a week after Paris attack on Twitter. Results indicate that professional mass media organizations still hold a greater agenda-setting power than individual opinion leaders for setting the public agenda, as they obtain significantly more tweets, mentions, and replies from the public. While the overall media agenda significantly correlates with the agenda set by the individual opinion leaders on Paris attack, time-series analysis reveals the intermedia agenda-setting effects between mass media and individual opinion leaders on Twitter are immediate and decrease as time elapses. Methodological and theoretical implications are discussed.

Uncovering User Interaction Dynamics in Online Social Networks

Measurement studies of online social networks (OSNs)show that all social links are not equal, and the strength of each link is best characterized by the frequency of interactions between the linked users. To date, few studieshave been able to examine detailed interactiondata over time. In this paper, we first analyze the interaction dynamics in a large online social network. We find that users invite new friends to interact at a nearly constant rate, prefer to continue interacting with friends with whom they have a larger number of historical interactions,and most social links drop in interaction frequency over time. Then, we use our insights from the analysis to derive a generative model of social interactionsthat can capture fundamental processes underlinguser interactions.