Multiplex Social Network Research Articles

Research on the characteristics of information propagation dynamic on the weighted multiplex Weibo networks

In order to simulate the forwarding situation of different categories of Weibo and discover interesting propagation phenomena in different layers of Weibo networks, this paper proposes the retweeting weighted multiplex networks and propagation model coupled with multi-class Weibo. Firstly, the weighted multiplex social network is constructed through the processing of Weibo network data. Secondly, a new information propagation model is established by using the weight and interlayer information of the Weibo multiplex network combined with the coupling factors in the propagation. Finally, the information propagation simulated by the propagation model is compared with the real data, so as to summarize different information propagation phenomena in multiplex social multiplex network. At the same time, by comparing the structure of the forwarding weighted multiplex network constructed by the short time data and the long time data, we find the self-similarity of the forwarding weighted multiplex network, which proves the generalization of the experiment. Through the above research, the mystery of the Weibo social network has been deeply explored, and a new perspective has been opened up for the exploration of social media information propagation.

Understanding the developmental transition between parallel and social play during preschool: A multiplex social network analysis.

Understanding the developmental transition between parallel and social play during preschool: A multiplex social network analysis.

Supplemental Material for Understanding the Developmental Transition Between Parallel and Social Play During Preschool: A Multiplex Social Network Analysis

Supplemental Material for Understanding the Developmental Transition Between Parallel and Social Play During Preschool: A Multiplex Social Network Analysis

Community detection based on nonnegative matrix tri-factorization for multiplex social networks

Abstract Nowadays, community detection is one of the important fields for understanding network topology and has many applications in information diffusion, interaction mining and migration behaviour analysis. Therefore, community detection in social networks can help to understand user behaviour and network characteristics. There are many community detection methods, which are often designed for single-layer social networks. However, real-world networks use several types of relationships to establish connections between users, each of which has different characteristics. Hence, real-world networks can be modelled as multiplex networks. In general, multiplex networks are an example of multilayer networks in which the relationships between users in different networks can be considered simultaneously. In these networks, each layer represents the connections between users in a social network. Meanwhile, communities in multiplex networks are identified based on the structure and connections between overlapping users in different layers of the network. In this article, the nonnegative matrix tri-factorization (NMTF) strategy is used to model multiplex social networks, and a solution for community detection is developed based on it. According to this strategy, a common consensus matrix and then an alignment matrix are extracted based on similarity metrics and network structure. The use of these matrices as a flexible modelling framework enables the detection of coherent community between overlapping users in multiplex social networks. We evaluate the proposed NMTF method through various metrics on a multiplex social network. The results of this evaluation show the better performance of NMTF in terms of community quality compared to equivalent methods.

Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities

Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency Between Embedding Vectors.

Online users are typically active on multiple social media networks (SMNs), which constitute a multiplex social network. With improvements in cybersecurity awareness, users increasingly choose different usernames and provide different profiles on different SMNs. Thus, it is becoming increasingly challenging to determine whether given accounts on different SMNs belong to the same user; this can be expressed as an interlayer link prediction problem in a multiplex network. To address the challenge of predicting interlayer links, feature or structure information is leveraged. Existing methods that use network embedding techniques to address this problem focus on learning a mapping function to unify all nodes into a common latent representation space for prediction; positional relationships between unmatched nodes and their common matched neighbors (CMNs) are not utilized. Furthermore, the layers are often modeled as unweighted graphs, ignoring the strengths of the relationships between nodes. To address these limitations, we propose a framework based on multiple types of consistency between embedding vectors (MulCEVs). In MulCEV, the traditional embedding-based method is applied to obtain the degree of consistency between the vectors representing the unmatched nodes, and a proposed distance consistency index based on the positions of nodes in each latent space provides additional clues for prediction. By associating these two types of consistency, the effective information in the latent spaces is fully utilized. In addition, MulCEV models the layers as weighted graphs to obtain representation. In this way, the higher the strength of the relationship between nodes, the more similar their embedding vectors in the latent representation space will be. The results of our experiments on several real-world and synthetic datasets demonstrate that the proposed MulCEV framework markedly outperforms current embedding-based methods, especially when the number of training iterations is small.

Emergency Warning Dissemination in a Multiplex Social Network

Emergency Warning Dissemination in a Multiplex Social Network

Correlation analysis of combined layers in multiplex networks based on entropy.

The interactions between layers of a multiplex network would generate new structural features, the most prominent feature being the existence of link overlaps between layers. How to capture the associations with the network behavior through the structural interaction between the combined layers of the multiplex network is a critical issue. In this paper, a new structure entropy is proposed by combining the overlapping links between the combined layers of a multiplex network. The correlation between layers is evaluated by structure entropy, and the results are consistent with the behaviors exhibited by the network. In addition, the validity and applicability of the proposed method were verified by conducting trials on four sets of real multiplex network data, which included the multiplex social network of a research department at Aarhus, tailor shop multiplex network, C. elegans multiplex network, and the network collected by Vickers from 29 seventh grade students in a school in Victoria.

Who influences whom and how in river-basin governance?A participatory stakeholder and social network analysis in Zayandeh-Rud basin, Iran

Power is a central concept in studying complex governance systems as it determines how stakeholders influence each other to achieve their preferred outcomes. Little is still known about complex power dynamics in water governance networks. Along this line, this study aims to investigate who influences whom and how in river-basin governance in the case of Zayandeh-Rud river basin (ZRB) in Iran. We applied a participatory stakeholder analysis (SA) and a social network analysis (SNA) to identify the relevant stakeholders and investigate their attributes and network-based characteristics in the ZRB's water governance system. A mixed-methods approach was used for information gathering using several interviews, focus groups, workshops, and a survey. We assembled different forms of power with interest to exclusively analyze the power dynamics in water governance system. We developed a multiplex social influence network comprising 24 stakeholders related together through four types of power-laden relationships with differing strengths. Our findings show that stakeholders with the same degree of power and interest may play different roles in the governance processes, which can be explained by their position and power relations in the multiplex network. We conclude that combining the results of SA and SNA analyses can contribute to better understanding of how water resources are governed and by whom, but a careful conceptualization of power is needed for meaningful interpretations. Our findings have important implications for diagnosing the power dynamics of stakeholders, conducting negotiations and conflict resolution processes, and facilitating social learning and mutual development of engagement strategies.

Dynamic interdependence of advice seeking, loaning, and recovery characteristics in recovery homes

ABSTRACT Recovery homes in the US provide stable housing for over 250,000 individuals with past histories of homelessness, psychiatric comorbidity and criminal justice involvement. We need to know more about how these settings help those who remain in recovery. Our study measured advice seeking and willingness-to-loan relationships, and operationalized them as a dynamic multiplex social network—multiple, simultaneous interdependent relationships—that exist within 42 Oxford House recovery homes over time. By pooling relationship dynamics across recovery houses, a Stochastic Actor-Oriented Modeling (SAOM) framework was used to estimate a set of parameters governing the evolution of the network and the recovery attributes of the nodes simultaneously. Findings indicated that advice and loan relationships and recovery-related attitudes were endogenously interdependent, and these results were affected exogenously by gender, ethnicity, and reason for leaving the recovery houses. Prior findings had indicated that higher advice seeking in recovery houses was related to higher levels of stress with more negative outcomes. However, the current study found that recovery is enhanced over time if advice was sought from residents with higher recovery scores. Our study shows that social embedding, i.e., one’s position in relationship networks affects recovery prospects. More specifically, the formation of ties with relatively more recovered residents is an important predictor of better outcomes.

Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics

Complex systems are fully described by the connectedness of their elements studying how these develop a collective behavior, interacting with each other following their inner features, and the structure and dynamics of the entire system. The forthcoming 6G will attempt to rewrite the communication networks’ perspective, focusing on a radical revolution in the way entities and technologies are conceived, integrated and used. This will lead to innovative approaches with the aim of providing new directions to deal with future network challenges posed by the upcoming 6G, thus the complex systems could become an enabling set of tools and methods to design a self-organized, resilient and cognitive network, suitable for many application fields, such as digital health or smart city living scenarios. Here, we propose a complex profiling approach of heterogeneous nodes belonging to the network with the goal of including the multiplex social network as a mathematical representation that enables us to consider multiple types of interactions, the collective dynamics of diffusion and competition, through social contagion and evolutionary game theory, and the mesoscale organization in communities to drive learning and cognition. Through a framework, we detail the step by step modeling approach and show and discuss our findings, applying it to a real dataset, by demonstrating how the proposed model allows us to detect deeply complex knowable roles of nodes.

Evolutionary dynamics of MEC’s organization in a 6G scenario through EGT and temporal multiplex social network

MEC is considered as a promising candidate to support the deployment of the 6G communication networks, enabling scalability, adaptability and resilience. We propose a modeling approach based on complex system to design a novel organizational aspect of mobile nodes acting as MECs in a 6G scenario, through the introduction of the temporal multiplex social network, which allows us to analyze proximity contacts, by embedding also the social aspects, and the evolutionary game theory. Our findings demonstrate how the proposed model gives a boost to MECs’ cooperative behavior, finding out an overall reduction of the blocking probability.

The Developmental Process of Peer Support Networks: The Role of Friendship.

This study investigated the characteristics and development of peer support networks in an effort to unravel the role of friendship in this developmental process. The relationships between friendship networks and peer support networks were explored, and the influence of dyadic and triadic friendships on the development of peer support relationships was examined. Two waves of data were collected among a sample of adolescents in six Chinese junior high schools (n = 913 students from 28 classrooms; mean age = 14.13 years; 50.49% boys), and classroom friendship networks and peer support networks were analyzed. The results showed that peer support networks were sparse, hierarchical, and sex-segregated. Furthermore, peer support networks and friendship networks partially overlapped. Friends tended to have similar support-seeking and support-providing ties. Longitudinal multiplex social network analysis revealed that peer support networks changed moderately over time, and friendships played various roles in the development of peer support networks. Dyadic friendships improved the formation of peer support ties. A mutual friend improved the formation of support relationships between two students when the mutual friend chose the two students as friends, but a mutual friend also hindered or had no effects on the formation of support relationships in other cases. The implications for educators to improve peer support networks are presented, and directions for future research are discussed.

Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

In the natural and social systems of the real world, various network can be seen everywhere. The world where people live can be seen as a combination of network with different dimensions. Link prediction formalizes the interaction behavior between people. Traditional link prediction methods mainly study the user behavior of static social network. This article studied the dynamic graph representation learning so as to put forward an improved link prediction model in dynamic social network. Besides, the interactions in the real world can be multiple, links at different moments may have different meanings. The proposed model firstly solved the problem of link prediction on multiple kinds of edges. The whole embedding of each node is separated into two parts, basic embedding and edge embedding. Then the proposed model selected time slices for dynamic social network to get the graph embeddings in different snapshots. What's more, the t+1 time step embedding vector was used to validate t time step prediction effect and the proposed model performed better than traditional graph representation learning methods.

Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior.

Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation; Durkheim, 1893). One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade (Kaplan and Hill, 1985.). Multilayer networks, high-dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi-dimensional social networks has recently garnered the attention of the network science community (Kivelä et al., 2014). Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions (Górski et al., 2018). Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich data sets, giving more accurate and complete pictures of social interactions.

Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors

With the growing popularity of online social networks, an environment has been set up that can spread rumors in a faster and wider manner than ever before, which can have widespread repercussions on society. Nowadays, individuals are joining multiple online social networks and rumors simultaneously propagating amongst them, thereby creating a new dimension to the problem of rumor propagation. Motivated by these facts, this paper attempts to address the rumor influence minimization in multiplex online social networks. In this work, we consider modeling the propagation process of such fictitious information as a significant step toward minimizing its influence. Thus, we analyze the individual and social behaviors in social networks; subsequently, we propose a novel rumor diffusion model, named the HISBmodel. In this model, we propose a formulation of an individual behavior towards a rumor analog to damped harmonic motion. Following this, the opinions of individuals in the propagation process are incorporated. Furthermore, the rules of rumor transmission between individuals in multiplex networks are incorporated by considering individual and social behaviors. Further, we present the HISBmodel propagation process that describes the spread of rumors in multiplex online social networks. Based on this model, we propose a truth campaign strategy in minimizing the influence of rumors in multiplex online social networks from the perspective of network inference and by exploiting the survival theory. This strategy selects the most influential nodes as soon as the rumor is detected and launches a truth campaign to raise awareness against it, so as to prevent the influence of rumors. Accordingly, we propose a greedy algorithm based on the likelihood principle, which guarantees an approximation within 63% of the optimal solution. Systematically, experiments have been conducted on real single networks crawled from Twitter, Facebook, and Slashdot as well as on multiplex networks of real online social networks (Facebook, Twitter, and YouTube). First, the results indicate the HISBmodel can reproduce all the trends of real-world rumor propagation more realistically than the models presented in the literature. Moreover, the simulations illustrate that the proposed model highlights the impact of human factors accurately in accordance with the literature. Second, compared to the methods in the literature, the experiments prove the efficiency of our strategy in minimizing the influence of rumors in the cases of single network and multiplex social network propagation. The results prove that the proposed method can capture the dynamic propagation process of the rumor and select the target nodes more accurately in order to minimize the influence of rumors.

The Interplay of Adolescents\u2019 Aggression and Victimization with Friendship and Antipathy Networks within an Educational Prosocial Intervention

How the interplay between peer relationships and behaviors unfolds and how this differs between classrooms is an understudied topic. This study examined whether adolescents befriend or dislike peers whom they consider as aggressor or victim and whether these results differ in classrooms that received an intervention to promote prosocial behavior compared to classrooms without the intervention. The sample was composed of 659 seventh graders (Mage = 12.32; 48% girls) from nine intervention and seven control classrooms in eight schools in Santiago, Chile. It was hypothesized that adolescents in intervention classrooms would be less befriended and more disliked by classmates who considered them as aggressors, and more befriended and less disliked by classmates who considered them as victims, compared to control classrooms. Longitudinal multiplex social network analyses (RSiena) indicate that antipathies toward peers considered as aggressive and victimized were significantly lower in intervention classrooms than in control classrooms, but no significant differences were found for friendships. These findings suggest that the impact of an educational intervention may go beyond changing individual behavior and extend to the way peer relations develop in classrooms.

The multiplexity of professional learning communities: exploring the co-evolution of teacher social networks

ABSTRACT Research on teacher professional learning communities (PLCs) has found that part of their benefit comes from increased collegiality. And yet, while the nature of such collegiality is theoretically multifaceted and includes a combination of instrumental, expressive and substantive relationships, this multidimensional nature of PLCs is almost never studied directly. In this paper, I fill that gap by examining the dynamics of relationship formation and evolution in a multiplex social network of 70 teachers during the first year of a small school reform. Using longitudinal social network models I analyse the within and between network dynamics of three types of discussion networks: course-related, personal, and, reform-related. Results show that the formation and evolution of ties within particular discussion networks were shaped by preference for similarity in demographic characteristics and attitudes. They also show that co-evolution between networks was shaped by reform-related discussions operating as a bridge connecting personal and course ties.

The Interplay Between Adolescents' Friendships and the Exchange of Help: A Longitudinal Multiplex Social Network Study.

The aim of this study was to unravel the interrelatedness of friendship and help, and to examine the characteristics of friendship and help networks. The effects of mutual versus one‐sided help relations on friendship initiation and maintenance, and vice versa, were examined. Friendship and help networks were analyzed (N = 953 students; 41 classrooms; M age = 12.7). The results illustrate that friendship and help networks show some similarities, but only partly overlap and have distinct characteristics. Longitudinal multiplex social network analyses showed that mutual help was important for the maintenance of friendship, but not for the initiation of friendship. Further, particularly mutual friendships provided a context in which help took place. Implications of these findings are discussed.