Dynamic Social Networks Research Articles

Facilitating the creation of compassionate cities in Flanders. The case of Bruges and Herzele.

This paper presents the case studies of Compassionate Bruges and Herzele, as experienced in the role of a project leader/facilitator. Two cities want to grow more solidarity and more carefulness for their citizens as an answer to social bitterness, coldness and isolation. Two PhD students search for an answer to the question ‘how can we develop and foster a civic approach that is complementary to institutional health care, improving the quality of life in situations of severe illness, dying and grieving?’. And to the question ‘how can we invite people to think and act differently to realise this social change?’.
 The uniqueness of this testimonial lies in the top-down approach that was chosen to implement the compassionate city as well as the compassionate community aspect of this project. This is unlike a lot of international examples of compassionate community projects that are rather driven by an individual ‘sponsor’ or grassroots initiatives. In the top-down approach, we bring together partners like schools, employers, life stance groups,… In the bottom-up approach we work with civil society organisations and key figures in local networks. We aim at developing a compassionate community in Bruges Sint-Kruis as well as in Herzele-Centre.
 We will give an overview of how the project was conceived and how both cities evolved in their own way, driven by local differences in policy making, social network dynamics and the people involved. We will present the tensions that emerged and what we did to try and manage these tensions, as well as to manage the complexity of the global context in which these projects deployed. 
 Since some of our interventions proved to be more successful than others, we will conclude with some learnings and reflections and a brief presentation of the ‘Navigation Triangle’ framework. This framework is the synthesis of the levers we experienced to be crucial to understand and use in the process of leading cultural change in a complex environment. This framework might inspire project leaders and facilitators in their navigation processes through the complexity of their own projects, trying to involve people from a top-down as well as a bottom-up point of view.

Impact of participation as a peer change agent on peer change agents themselves: a quantitative study of a peer-led, social media-based PrEP promotion intervention.

Despite the adoption of pre-exposure prophylaxis (PrEP) as a crucial HIV intervention, uptake remains suboptimal among men who have sex with men, a sexual minority group, due to barriers like cost and stigma. Peer change agents (PCAs) disseminate PrEP information within their social networks. This study explores the reciprocal effects of an online community-based participatory intervention on PCAs, focusing on their transformed PrEP uptake perceptions-leadership efficacy, social network dynamics, attitudes, perceived benefits and barriers and self-efficacy. Leveraging insights from the PrEP Chicago Study, our research addresses a key gap in community-based participatory interventions for PrEP uptake: the transformative experiences and perception shifts of PCAs involved in these interventions. We engaged 20 men who have sex with men, aged 18-45, as PCAs in a one-group pretest-posttest design intervention, which disseminated PrEP communications within their preferred online networks. We utilized the PrEP Chicago Study's 45 Likert items, tailored to reveal the PCAs' transformative potential. Data on PrEP uptake perceptions, sociodemographics and social media use were captured and analyzed using the Wilcoxon matched-pairs signed-rank test, a nonparametric method. PCAs demonstrated significant changes in their PrEP uptake perceptions, including leadership capacity, social network dynamics, attitudes toward PrEP, perceived benefits, barriers and self-efficacy. Our intervention highlights the reciprocal transformation PCAs undergo when disseminating PrEP information. This study adds a new dimension to community-based PrEP interventions and underscores the need for continued refinement of peer-led strategies to optimize the transformative potential of PCAs.

Features of Recommendation Algorithm on Base of Analysis of Social Network Data Mining Methods

In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features. SA allows the system to tailor recommendations based on sentiment analysis, offering users items that match their expressed emotions. Experiments show significant improvements in recommendation accuracy when sentiment data is integrated. TM allows the system to understand the main concerns of users by identifying dominant themes, thereby providing personalized recommendations and staying abreast of evolving trends. At the same time, AFM identifies influential users and communities, increasing relevance and awareness of system elements. The article highlights the enormous potential of social networks for the development of effective, personalized recommendation systems. Using sentiment analysis and topic modeling, these systems can provide personalized and relevant recommendations based on public sentiment, trending topics, and social network dynamics.

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications.

Personal networks and the politics of belonging: Refugee integration in Thessaloniki Greece

AbstractRefugees use social networks to navigate the institutions of immigration. Social networks also serve as support structures as refugees claim their identity and establish a sense of belonging. This paper examines the influence of personal social networks on the integration experiences of 16 asylum‐seekers in Thessaloniki, Greece. Case studies of three recent asylum‐seekers convey how the dynamics of personal social networks differentially influence the experiences of integration. The visualizations of their personal social network, the 45 individuals they engage with routinely, reveal the structure and composition of their social lives. Across the study sample, refugees with more Greek contacts were better able to navigate the economic and legal landscape. However, more engagement with host nationals did not necessarily improve feelings of belonging. Migration policies and programming should consider how to use social networks, both existing and new, to improve migrant integration outcomes.

Simulating emergence of novelties using agent-based models

Understanding the growth and evolution of social networks is an important area of study, as these networks form the foundation for many popular online services such as social networking sites (SNS) and online games. However, previous models developed to explain the growth mechanisms of these networks have struggled to accurately reproduce certain behaviors that are frequently observed in real data, such as waves of novelty, in which new individuals or topics receive more attention than existing ones for a short period of time. In this study, we introduce a new model that incorporates context information into existing agent-based models in order to more accurately capture the structure and growth dynamics of these networks. Context information is introduced through labels based on the timing of appearance and relationships with antecedent agents. New agents are first added to the network when they are called by existing agents, and at this time they are also given a label. Agents added to the network at the same time by the same agent will have the same label. These labels are used to classify agents and give them different selection probabilities. This newly introduced selection probability creates a mechanism in which new agents receive attention beyond preferential attachment. By comparing the results of our model with real data on ten metrics, we demonstrate that it is able to produce behavior that more closely resembles real data. This improved understanding of the dynamics of social networks has important implications for designing effective interventions, including strategies for user acquisition and retention.

The Role of Trust in Dependence Networks: A Case Study

In a world where the interconnection and interaction between human and artificial agents are continuously increasing, the dynamics of social bonds and dependence networks play a fundamental role. The core of our investigation revolves around the intricate interplay between dependence and trust within a hybrid society, populated by human and artificial agents. By means of a structural theory, this study offers valuable insights into the utilization of dependence networks and their impact on collaborative dynamics and resource management. Most notably, agents that leverage dependence, even at the cost of interacting with low-trustworthiness partners, achieve superior performance in resource-constrained environments. On the other hand, in contexts where the use of dependence is limited, the role of trust is emphasized. These findings underscore the significance of dependence networks and their practical implications in real-world contexts, offering useful practical implications in areas such as robotics, resource management, and collaboration among human and artificial agents.

Exploring the Convergence of Design, Security, and Human Dynamics in Social Networks in India

This Exploration dives into the complex exchange between social networks, zeroing in on their design, security measures, and the impact of human factors. As social networks keep on molding present day correspondence and connection, understanding their elements turns out to be progressively pivotal. The examination investigates the architecture and user-focused design rules that improve user experiences, encourage community associations, and empower self-expression. All the while, the review tends to the squeezing security challenges presented by these platforms, dissecting risks like identity theft and misinformation dispersal, and proposing techniques for powerful encryption and data protection. Moreover, the review digs into the domain of human behavior and brain research, clarifying how individual inspirations, cognitive biases, and social standards add to content creation, sharing, and commitment inside social networks. The bits of knowledge acquired give a complete perspective on what user activities mean for network elements and the development of online networks. By exploring these aspects, the review expects to illuminate platform developers and policymakers, offering important direction on upgrading design, carrying out powerful security measures, and figuring out user behavior. Eventually, this examination adds to an all-encompassing comprehension of social networks, introducing a diverse investigation of design, security, and human factors. The discoveries guide the advancement of more user-centric, secure, and socially significant social organization platforms that line up with user inclinations and contribute decidedly to the digital landscape.

The Memory Influence on Opinion Dynamics in Coopetitive Social Networks: Analysis, Application, and Simulation

In social psychology, it is well known that the evolution of opinion dynamics in a society depends not only on the current opinions of individuals, but also on the past opinions stored in memory. However, it is still difficult to clearly characterize and analyze the memory influence on opinion dynamics in social networks, especially in coopetitive (cooperative and competitive) networks. In this paper, we propose a novel model of opinion dynamics to describe the memory influence in coopetitive networks. In this model, memory and memoryless communication rules are simultaneously proposed for the opinion update mechanism. In terms of the network topological structure and the graph spectrum, sufficient and/or necessary conditions are respectively established to ensure that the opinion is nonstationary polarization, nonstationary consensus and neutral. We employ the Kahneman's seminal experiment on choices in risky and riskless contexts to interpret the results obtained in the present paper. Simulation analysis shows that the memory capacity of the individual is inversely proportional to the speeds of the ultimate opinions formational, and the opinions reach a persistent disagreement when the individuals are very confident.

DySuse: Susceptibility estimation in dynamic social networks

DySuse: Susceptibility estimation in dynamic social networks

Multi-objective based unbiased community identification in dynamic social networks

A network is a topological arrangement of its two basic elements, nodes and edges. Networks in the real world are not static. They tend to evolve with time, causing the set of nodes and edges to alter as well. They consist of several hidden bits of data whose analysis have drawn significant research interest. Identifying groups of similar nodes or edges helps in gaining knowledge about their interaction patterns. These groups are known as communities, which can be disjoint or overlapping. The dynamic nature of the network also impact its current community structure and makes it difficult to keep track of them. The paper presents a multi-objective optimization approach for identifying community structure in a dynamic network. A network is considered as a series of events generated over time, where each event is a new edge introduced at a time. The proposed algorithm uses three objective functions that are inspired from network properties. The community of a node corresponding to an input edge is updated by an algorithm based on its newness. The algorithm uses the Pareto front principle to identify the optimal community. The algorithm is evaluated over 12 datasets and compared to 10 state-of-the-art algorithms. It shows superior performance on real and connected datasets and also performs well for disconnected datasets. The algorithm is evaluated using both accuracy and quality metrics, with the quality metrics slightly outweighing the accuracy metrics.

Consensus-based generalized TODIM approach for occupational health and safety risk analysis with opinion interactions

Occupational health and safety (OHS) risk analysis serves as a foundation for identifying, preventing, and controlling OHS hazards to reduce occupational accidents. As a representative risk analysis approach, Fine-Kinney has been commonly applied to control hazards. However, current Fine-Kinney studies ranked hazards without considering the consensus reaching process (CRP) with incomplete information, insufficient to tackle decision makers’ (DMs’) dissatisfaction. Besides, risk analysis mainly relies on DMs’ subjective assessments, where opinion interactions inevitably exist because of DMs’ communication during the assessment process. This paper aims to develop a hybrid generalized TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach in the Fine-Kinney framework, integrating CRP with dynamic social influence network (SIN), and probabilistic linguistic terms (PLTSs). The PLTSs are used to cope with the complex and incomplete DMs’ opinions. The dynamic SIN is proposed to calculate the weights of DMs and describe the opinion interactions considering the psychological behaviors of DMs. Then, a new CRP is developed including a two-fold personalized feedback mechanism based on the dynamic SIN. The generalized TODIM method is introduced to rank all identified potential occupational hazards based on the collective opinions after CRP. Finally, a numerical example is conducted to verify the efficiency of the proposed approach. Comparison and sensitivity studies are also carried out to test the rationality and efficiency of the proposed approach.

Opinion evolution and dynamic trust-driven consensus model in large-scale group decision-making under incomplete information

The shift to a new era of dealing with big data has driven continuous progress and development in computer science, artificial intelligence and machine learning. This change has led to the application of advanced techniques in the realm of decision science, particularly in the area of large-scale group decision-making (LSGDM). However, although these existing techniques have become the core of LSGDM methods, they are still limited in solving problems facing incomplete data. In addition, due to the rise of social media platforms such as Weibo, WeChat and Twitter, which build bridges for communication between decision makers (DMs), this brings new opportunities and challenges for consensus research. To address this set of issues, this study develops a consensus architecture that combines dynamic social network and opinion evolution in the context of an incomplete multi-attribute LSGDM. It is worth mentioning that the proposed consensus framework is a novel decision-making system that can be used to complete the estimation of the missing values and the consensus reaching process (CRP) by simulating the realistic decision-making scenarios. Firstly, considering the size of the trust value and the length of the path, a new trust propagation method is designed to achieve a more reliable estimation of the unknown trust value. Secondly, this paper establishes a missing value estimation method by virtue of the improved DeGroot model, which is able to obtain complete evaluation information by simulating the opinion formation process of DMs. Next, a hierarchical clustering algorithm with stronger robustness is constructed, which not only can adaptively complete the clustering process, but also integrally considers two attributes of trust and opinion similarity. In light of the above research, this study designs an opinion evolution and dynamic trust-driven consensus model, referred to as the DSN-DG-LSGDM model. Finally, the sensitivity analysis and experiments on a real dataset verify the significant superiority of the constructed DSN-DG-LSGDM model compared with the extant LSGDM consensus models.

Socio-economic inequalities in smoking and drinking in adolescence: Assessment of social network dynamics.

We investigated whether (1) adolescents selected friends with a similar socio-economic status (SES), (2) smoking and alcohol consumption spread in networks and (3) the exclusion of non-smokers or non-drinkers differed between SES groups. This was a longitudinal study using stochastic actor-oriented models to analyze complete social network data over three waves. Eight Hungarian secondary schools with socio-economically diverse classes took part. This study comprised 232 adolescents aged between 14 and 15 years in the first wave. Self-reported smoking behavior, alcohol consumption behavior and friendship ties were measured. SES was measured based upon entitlement to an income-tested regular child protection benefit. Non-low-SES adolescents were most likely to form friendships with peers from their own SES group [odds ratio (OR) = 1.07, 95% confidence interval (CI) = 1.02-1.11]. Adolescents adjusted their smoking behavior (OR = 24.05, 95% CI = 1.27-454.86) but not their alcohol consumption (OR = 1.65, 95% CI = 0.62-4.39) to follow the behavior of their friends. Smokers did not differ from non-smokers in the likelihood of receiving a friendship nomination (OR = 0.98, 95% CI = 0.87-1.10), regardless of their SES. Alcohol consumers received significantly more friendship nominations than non-consumers (OR = 1.16, 95% CI = 1.01-1.33), but this association was not significantly different according to SES. Hungarian adolescents appear to prefer friendships within their own socio-economic status group, and smoking and alcohol consumption spread within those friendship networks. Socio-economic groups do not differ in the extent to which they encourage smoking or alcohol consumption.

Modeling dynamic social networks using concept of neighborhood theory

Dynamic social network analysis basically deals with the study of how the nodes and edges and associations among them within the network alter with time, thereby forming a special category of social network. Geometrical analysis has been done on various occasions, but there is a difference in the approximate distances of nodes. Snapshots for social networks are taken at each time slot and then are bound for these studies. The paper will discuss an efficient way of modeling dynamic social networks with the concept of neighborhood theory of cellular automata. So far, no model that uses the concept of neighborhood has been proposed to the best of our knowledge and the literature survey. Besides cellular automata that has been important tool in various applications has remained unexplored in the area of modelling. To this extent the paper, is the 1st attempt in modelling the social network that is evolving in nature. A link prediction algorithm based on some basic graph theory concepts has also been additionally proposed for the emergence of new nodes within the network. Theoretical and programming simulations have been explained in support to the model. Finally, the paper will discuss the model with a real-life scenario.

Can participatory processes lead to changes in the configuration of local mental health networks? A social network analysis.

Systems modeling offers a valuable tool to support strategic decision-making for complex problems because it considers the causal inter-relationships that drive population health outcomes. This tool can be used to simulate policies and initiatives to determine which combinations are likely to deliver the greatest impacts and returns on investment. Systems modeling benefits from participatory approaches where a multidisciplinary stakeholder group actively engages in mapping and contextualizing causal mechanisms driving complex system behaviors. Such approaches can have significant advantages, including that they may improve connection and coordination of the network of stakeholders operating across the system; however, these are often observed in practice as colloquial anecdotes and seldom formally assessed. We used a basic social network analysis to explore the impact on the configuration of the network of mental health providers, decision-makers, and other stakeholders in Bogota, Colombia active in a series of three workshops throughout 2021 and 2022. Overall, our analysis suggests that the participatory process of the systems dynamics exercise impacts the social network's structure, relationships, and dynamics.

A Bibliometric Analysis of Social Justice Movements and Popular Culture Intersection on Social Media

Using a bibliometric analysis, this study conducts a thorough investigation of the relationship between social justice movements, popular culture, and social media. Utilizing citation analysis, co-authorship networks, keyword co-occurrence, and temporal trends analysis, the study draws from a wide spectrum of scholarly publications, including seminal texts and modern contributions. The complex relationships found in the literature are made easier to show with the help of the visualization application VOSviewer. The analysis identifies important research areas and highlights topics including the effects of youth internet use, the contribution of popular culture to activism, and social network dynamics. The intellectual landscape is also shaped by important works by writers like Tarrow, McPherson, and Collins. Along with highlighting justice, race, and gender, the study also uncovers rising themes like diversity and group action through term occurrences. All things considered, this bibliometric analysis offers a thorough grasp of the intellectual landscape, generating a nuanced knowledge of the complex convergence of social justice, popular culture, and social media and providing ideas for future research.

Socioeconomic inequalities in smoking and drinking in adolescence: social network dynamics

Abstract Background Network-based interventions have the potential to prevent socioeconomic inequalities in health behaviors but require a good understanding of the underlying social network mechanisms. We investigated whether (1) adolescents selected friends with a similar socioeconomic status (SES), (2) smoking and alcohol consumption spread in networks, and (3) the exclusion of non-smokers or non-drinkers differed between SES groups. Methods We applied Stochastic Actor Oriented Models to the complete social network data (“Wired into Each Other” data) of 253 adolescents in eight Hungarian secondary school classes over three waves between 2010 and 2013. SES was measured based on entitlement to an income-tested regular child protection benefit. Results High SES adolescents were most likely to form friendships with peers from their own SES group (odds ratio [OR] = 1.1, 95% Confidence Interval [CI] = 1.0-1.1). Adolescents adjusted their smoking behavior (OR = 24.0, 95% CI 1.3-454.9) but not their alcohol consumption (OR = 1.6, 95% CI 0.6-4.4) to emulate the behavior of their friends. Smokers did not differ from non-smokers in the likelihood of receiving a friendship nomination (OR = 1.0, 95% CI 0.9-1.1), regardless of their SES. Alcohol consumers received significantly more friendship nominations than non-consumers (OR = 1.2, 95% CI 1.0-1.3), but this association was not significantly different according to SES. Conclusions Socioeconomic inequalities in health behaviors were driven by preferences to form friendships within their own SES group and the spread of smoking and alcohol consumption in the network. These inequalities were not amplified by SES differences in encouraging smoking or drinking. Main messages: In three waves of observations in a Hungarian adolescent cohort, the study found social network effects for inequalities in smoking and alcohol consumption. Key messages • Inequalities in health behaviors arise because adolescents with high socioeconomic backgrounds selected each other as friends and smoking/alcohol consumption spread in networks. • Inequalities were not due to socioeconomic differences in encouraging harmful behaviors.

Modularity-based approach for tracking communities in dynamic social networks

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks.

A one-stage memetic algorithm for jointly detecting hierarchical and overlapping community structures in dynamic social networks

A one-stage memetic algorithm for jointly detecting hierarchical and overlapping community structures in dynamic social networks