Social Network Model Research Articles

Mobile HIV Testing Through Social Networking Platforms: Comparative Study.

BackgroundImproving HIV screening in key populations is a crucial strategy to achieve the goal of eliminating AIDS in 2030. Social networking platforms can be used to recruit high risk-taking men who have sex with men (MSM) to promote the delivery of voluntary counseling and testing (VCT) as mobile HIV testing. Therefore, client recruitment and availability of mobile HIV testing through social networking platforms requires further evaluation.ObjectiveThe aim of this study is to compare the effects of targeting high risk-taking MSM and HIV case finding between two mobile HIV testing recruitment approaches: through the traditional website-based approach and through social networking platforms.MethodsA comparative study design and propensity score matching was applied. The traditional VCT model, that is, the control group, recruited MSM through a website, and a trained research assistant visited the walk-in testing station at a gay village on Friday and Saturday nights. The social networking VCT model, the experimental group, recruited MSM from social networking platforms by periodically reloading into and conducting web-based discussions on dating apps and Facebook. The participants then referred to others in their social networks via a popular messenger app in Taiwan. The test was conducted at a designated time and place during weekdays by a trained research assistant. Across both modes of contact, before the mobile HIV testing, participants needed to provide demographic characteristics and respond to a questionnaire about HIV risk-taking behaviors.ResultsWe recruited 831 MSM over 6 months, with a completion rate of 8.56% (616/7200) in the traditional VCT model and 20.71% (215/1038) in the social networking VCT model. After propensity score matching, there were 215 MSM in each group (mean age 29.97, SD 7.61 years). The social networking model was more likely to reach MSM with HIV risk-taking behaviors, that is, those seeking sexual activity through social media, having multiple sexual partners and unprotected anal intercourse, having experience of recreational drug use, and never having or not regularly having an HIV test, compared with the traditional model. HIV positive rates (incidence rate ratio 3.40, 95% CI 1.089-10.584; P=.03) and clinic referral rates (incidence rate ratio 0.03, 95% CI 0.001-0.585; P=.006) were significantly higher among those in the social networking VCT model than in the traditional VCT model.ConclusionsThrough effective recruitment strategies on social networking platforms, the social networking VCT mode can be smoothly promoted, as compared with the traditional VCT model, to target high risk-taking MSM and increase testing outcomes.

Spreading Curricular Reform: A Social Network Analysis

The purpose of this study was to inform not only the education field but also any industry undergoing large-scale reform of the types of organizational roles and relational ties that drive momentum. This study offers an explanation for the spread of organizational change in the context of teacher education reform, specifically in that of University–School Partnerships in the Renewal of Educator Preparation (US Prep). Knowledge of relationships in organizational change informs administration, policy makers, and curriculum change agents of requisite social structures to launch the programs they want to grow, and halt the programs they want to stop. Through social network modeling and a quantitative analysis of social capital exchange, this study illustrates the relational ties within two large state universities implementing the US Prep curricular reform model. The findings of this study support the social network theory of the strength in weak ties or that loosely connected networks foster greater change momentum. The social network analysis illustrates the integral roles and relationships driving the expansion of the coalition and suggests to policy makers that these roles and the types of social capital exchanged are imperative for change efforts that spread.

Research on Spatial Correlation Characteristics and Their Spatial Spillover Effect of Local Government Debt Risks in China

Scholars have proposed a series of methods, such as “sustainability of local government debt”, to measure local government debt risks. However, these methods have caused a lot of controversy. Based on a macro balance sheet, this study uses an improved “distance to distress” to measure China’s local government debt risks and applies a social network model to identify the spatial correlation characteristics, as well as the spillover effect. The results are as follows: (1) The data show multiple and heterogeneous spatial correlations for China’s local government debt risks; (2) there are some similarities between the subgroups and seven major geographic regions in China. The links among subgroups are randomly distributed and external; (3) the data manifest a “small world”, with a decreasing transitivity since 2014; (4) between these two significant factors, the positive impact of local government competition is more obvious than the division of powers and responsibilities; and (5) the spatial spillover effect of China’s local government debt risks results from the combination of local government competition, the division of powers and responsibilities, and local government intervention. This paper provides a scientific basis for obtaining a deeper understanding of China’s local government debt risks, and puts forward policy recommendations to strengthen China’s debt management.

A propagation trust model in social networks based on the A* algorithm and multi-criteria decision making

The rapid growth of social networks facilitates the exchange of information whereas malicious behaviors are also steadily increasing in these ecosystems. This results in a challenging situation for individuals to trust other parties. This paper studies the propagation of trust within a chain of trust relations to calculate the trust values of existing users. In this research, an approach for the precise selection of trustworthiness paths as well as the integration of indirect trust values based on the most reliable routes is introduced. The presented approach fuses the ideas from the A* algorithm and multi-criteria decision making approaches using (i.e. TOPSIS method) under fuzzy environments for finding the most reliable path. Moreover, for selecting the most appropriate middle node, a set of criteria such as topological similarity, profile similarity, Dunbar’s theorem, local trust, and contextual trust are considered. The evaluation results of the proposed approach demonstrate the propagated trust distance with the different average path lengths while preserving the accuracy of the inferred trust values between each unconnected pair of nodes. The evaluations are performed using the Facebook and Twitter networks having different topologies and the results are compared to the TidalTrust and the MoleTrust algorithms.

Phase II monitoring of autocorrelated attributed social networks based on generalized estimating equations

Social network monitoring has attracted the attention of many researchers in the area of statistical process monitoring. It is essential to consider the categorical attributes of communications between people in the modeling of social networks. Social network communications with categorical attributes can be summarized in contingency tables and it is more realistic to work with the communications as time-dependent variables. In the present study, since the autocorrelation between social networks can significantly alter the statistical performance of different monitoring approaches, generalized estimating equations are used to estimate model parameters considering autocorrelation between social networks. Moreover, two control charts, Hotelling’s T2 and the multivariate exponentially weighted moving average, are developed to monitor model parameters in Phase II. The performance of the proposed method is evaluated through simulation studies in terms of the average run length criterion. Finally, the application of the proposed method is shown through a numerical example and a real case

The Co-evolution of online social networks and syphilis incidence among young black men who have sex with men.

Network contexts have been implicated in the facilitation of syphilis transmission, however little is known about the relationship between online social networks, a proxy for physical networks, and rates of syphilis infection and how they co-evolve over time. To these ends, this study explored the interdependent relationship between Facebook friendship network dynamics and rates of syphilis incidence among young Black men who have sex with men (YBMSM). Specifically, we investigated the extent to which syphilis status, as a latent profile of network members, serves as a selection mechanism of Facebook tie formation and the degree to which Facebook friendships influenced dynamics of syphilis seroconversion. Sexual health, behavioral, and social network data were collected from a cohort of YBMSM (N=286, Mean age=22.8) at two time points between 2013 and 2015 in Chicago, IL, USA. The interdependencies between Facebook friendship networks and syphilis infection were assessed using stochastic actor-based models for social networks and behavior. Results showed that YBMSM tended to form Facebook friendships with other YBMSM who had similar syphilis and HIV status profiles, and the hazard of contracting syphilis was likewise influenced by their Facebook friendships, albeit subtly, by being connected to infectious network members. Meanwhile, intrinsic characteristics like condomless sex, HIV status, and online partner-seeking were not associated with dynamics in syphilis incidence. These findings show that there is a mutually dependent relationship between the online network dynamics of YBMSM and rates of syphilis incidence. Findings also direct attention to network-based strategies for intervention.

Теоретические основы прогнозирования пользовательских настроений в социальных сетях

Analyzed is the problem of predicting the mood of users in social networks. The properties of social network information objects are investigated, and a method for collecting and processing social data for forecasting is presented. The goal of this study is to prove or disprove the possibility of predicting user sentiment in social networks. A discrete model of the social network has been compiled. The theoretical foundations of forecasting are formulated based on the statistical characteristics of the discrete model, as well as algorithms for searching and systematizing information objects. A method is proposed and described that allows you to predict the reaction of users to a message published in a social network. A hypothesis about the possibility of predicting mood in social networks is formulated and proved. The possibility of forecasting user sentiment in social networks is proved. A new method that allows you to predict the reaction of users to a published news item is presented based on the principles of machine learning. The results of the work can be applied in the field of small and medium-sized businesses when directly searching for a target audience to increase the profitability of the enterprise. The development of sentiment analysis methods will allow planning measures to contain or speed up the spread of messages. Also, the application value in developing a forecasting model may lie in the development of news sources or identifying trends. The scientific significance of the work lies in the presentation of a method that allows predicting the reaction of social network users to the published news. The results of this work can be used to improve the performance of social networks and to solve the problem of “cold” start (what to show to new users).

Presenting the proposed model of health information social network

Presenting the proposed model of health information social network

Social percolation revisited: From 2d lattices to adaptive networks

The social percolation model Solomon et al. (2000) considers a 2-dimensional regular lattice. Each site is occupied by an agent with a preference xi sampled from a uniform distribution U[0,1]. Agents transfer the information about the quality q of a movie to their neighbors only if xi≤q. Information percolates through the lattice if q=qc=0.593. – From a network perspective the percolating cluster can be seen as a random–regular network with nc nodes and a mean degree that depends on qc. Preserving these quantities of the random–regular network, a true random network can be generated from the G(n,p) model after determining the link probability p. I then demonstrate how this random network can be transformed into a threshold network, where agents create links dependent on their xi values. Assuming a dynamics of the xi and a mechanism of group formation, I further extend the model toward an adaptive social network model.

A Review of Latent Space Models for Social Networks

In this paper, we provide a review on both fundamentals of social networks and latent space modeling. The former discusses important topics related to network description, including vertex characteristics and network structure; whereas the latter articulates relevant advances in network modeling, including random graph models, generalized random graph models, exponential random graph models, and social space models. We discuss in detail several latent space models provided in literature, providing special attention to distance, class, and eigen models in the context of undirected, binary networks. In addition, we also examine empirically the behavior of these models in terms of prediction and goodness-of-fit using more than twenty popular datasets of the network literature.

Advanced Community Identification Model for Social Networks

Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this study, we propose an advanced community-detection model for social networks in order to identify network communities based on global and local information. Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation. This process is conducted with the same color till nodes reach maximum similarity. Finally, the communities are formed, and a clear community graph is displayed to the user. Our proposed model is completely parameter-free, and therefore, no prior information is required, such as the number of communities, etc. We perform simulations and experiments using well-known synthetic and real network benchmarks, and compare them with well-known state-of-the-art models. The results prove that our model is efficient in all aspects, because it quickly identifies communities in the network. Moreover, it can easily be used for friendship recommendations or in business recommendation systems.

Social Networks through the Prism of Cognition

Human relations are driven by social events—people interact, exchange information, share knowledge and emotions, and gather news from mass media. These events leave traces in human memory, the strength of which depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel cognition‐driven social network (CogSNet) model that accounts for cognitive aspects of social perception. The model explicitly represents each social interaction as a trace in human memory with its corresponding dynamics. The strength of the trace is the only measure of the influence that the interactions had on a person. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves the quality of modeling of human interactions in social networks.

Advanced Service Search Model for Higher Network Navigation Using Small World Networks

Social Internet of Things (SIoT) is a new standard resulting from the integration of the Internet of Things (IoT) and social networking. IoT is a visionary, one-paradigm, whereas social networks are platforms where voluminous collaborations between humans exist. The SIoT is defined as a social network of objects that are not only smarter but also socially conscious. Fundamental requirements of both IoT and SIoT networks include efficient service search and the discovery of object mechanisms. Therefore, our paper proposes a simple model for social networks to discover the object or services by using a small-world network. Our proposed model comprised a set of objects, and where each object is looking for a service. The service search is initiated in different hops by an object using a service query message to the nearest object. If the requested object is identified immediately or at the first hop, a permanent link is established between the service requester and the service provider otherwise the search process is repeated until the service is discovered. In this study, we integrate the SIoT with the small world concept for the building of our model. Our proposed model guarantees that the object containing the information is in a bounded path length and is treatable owing to the structure of small-world networks. The process of search is efficient because it is initiated only when an object asks for another object or service. Our intention here is to increase the navigability of the network. We carefully performed numerical analyses for our model and presented the simulation results based on efficiencies such as average path length, clustering coefficient, service execution time, and the giant component. After conducting various experiments, we conclude that our proposed model is efficient and reflects the real network structures of small-world networks, therefore, it can be suitable for social networks.

Visualization and Interpretation of Resilient Safety Culture: Integrated Social Network Modeling

Visualization and Interpretation of Resilient Safety Culture: Integrated Social Network Modeling

MAPPING FEAR OF CRIME THROUGH SOCIAL NETWORK MODEL

The present study analyzes fear of crime through social network model. The social network model is delimited to three dimensions i.e., private social network (PrSN), parochial social network (PaSN), and public social network (PbSN). The association and contribution of each of the dimension is estimated through binary logistic regression. Data for the study is collected from 298 out of 1186 employees and students of the Main Campus of Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa-Pakistan. Findings of the study show that the three dimensions are inversely related to the fear of crime. It means that improvement in private, parochial, and public social networks reduces fear of crime. Further, it is observed that private and public social networks are contributing more than parochial social network in reducing fear of crime in Pakhtun society of district Mardan. Thus, it is inferred that social networks through the development of a sense of empowerment among the members decrease fear of crime. Stronger social networks act as a social control mechanism and reduce the likelihood of the occurrence of deviant, and/or criminal behavior in a society

ASSESSING THE DYNAMICS OF DISSEMINATION OF INFORMATION MESSAGES ACCORDING TO THE DATA OF ELECTRONIC MASS MEDIA

The main directions of destructive information and psychological influence of the enemy on the population of Ukraine, leaders and personnel of the military administration, the Armed Forces of Ukraine and other law enforcement agencies, issues of the analysis of the dissemination of information messages (content) of the electronic media which contain the destructive information and psychological impact are considered. The model of social networks as a means of mass communication, which is used for the realization of destructive information and psychological influence is considered. The main components of the model are the user, his thoughts (views), influence, trust and reputation. The process of influencing to the user by means of social networks through innovation and its dissemination is considered. Indicators that can be used to characterize the process and to evaluate the impact: “likes”, “dislikes”, “reposts”, “views”, and “comments” are also provided. The process of tracking the destructive influences contained in information messages from the point of view of destructive informational and psychological influence is described. The system of indicators of dynamics of distribution of information messages on the Internet is analyzed. The necessity of fixing on certain points of time and use of additional indicators: “number of drawings”, “absolute growth”, “growth rate”, “growth rate” is grounded and the order of their calculation is given. The logical and structural scheme of calculating the dynamics of information message dissemination by means of the Internet has been developed. It is determined that to solve the problem of automation of tracking and visualization of the dynamics of information dissemination requires specialized software that will read the primary indicators from certain publications of electronic communications and social networks and calculate the proposed indicators of the dynamics of information message propagation.

Machine Learning Methods for Analysing the Impact of Social Media on Students Academic Performance

Today Social networking is a common apporch, mostly along with youngster. The social media impact on the academic performance of students become fundamental part to determine. Social Networking Sites (SNS) for example, twitter facebook are presently an vital medium that are used to connect peoples and different associations approximately the world Technology is thriving quick with the passage of time, and the youngsters are trapped in this swift revolutionize. In this paper we find out in which ways social sites affects student’s academic performance and recognize the impact of social sites on our educational system. The reason at the back huge use of social networking sites. Social sites networking (SNS) have grow from being merely libertine stages for personl use to awesome progressive structure that utilized both blend and inside and remotely for relationship and joined exertion with shafts separated stakeholders. Generally among youthful understudies, Social sites networks is an unmistakable example today In this paper by using student-por.csv and student-mat.csv dataset, we work on the classifier on the social media impact on students academic performances. For this purposes we choose K nearest-neighbor (KNN), support vector machine (SVM) and Linear regression algorithm using python that predict slightly better and give right prediction about the student’s academic performance. In social networking model our work has enhanced our imminent. The results reveal that there is strong relationship between social media and student’s academic performance. This paper shows that mostly students have cell phones and also have internet availability so they use 40 mints form 4 hours daily and not give attentions to their studies, all these activities on internet effect the student’s academic performance. This study will commence with introduction and background of existing studies. After that on the basis of background there will be a research question.

Who’s in Charge? Designing Experiential Project Courses to Expose Students to the Multi-Stakeholder World of Work Today

Experiential field-based learning (FBL) courses have an increasing presence in business school programs. Tools are needed to help with the effective design, analysis and curriculum development of this multi-stakeholder project-based pedagogy. The FBL Social Network Model can be used for this type of analysis and can be applied in various ways. This article reviews this framework, how to deploy it and its relevance around effectively designing FBL courses to prepare students for the multi-dimensional demands of the digital world of work today.

Stakeholders’ power over the impact issues of building energy performance gap: A two-mode social network analysis

Abstract Energy-related stakeholders play a critical role in energy-saving and emission reduction. However, most previous studies focused on only a few stakeholders (e.g., occupant, designer and owner). Therefore, there is a lack of systematic analysis of energy-related stakeholders. Improving building energy efficiency requires the collaboration of all stakeholders in the building lifecycle. This study applies a two-mode social network model to investigate the stakeholders’ power over the impact issues of building energy efficiency. Results show that all issues can be addressed by at least two stakeholders, thereby indicating the potential of collaboration in building energy management. Designers, manufacturers, commissioning agents and researchers are the most influential stakeholders with the highest degree centrality, betweenness centrality and eigenvector centrality. Insufficient knowledge and experience, lack of information integrity and unclear responsibility are the most influential impact issues. With the density of interaction being 1.000, nine core stakeholders (i.e. owner, designer, contractor, subcontractor, supervisor, manufacturer, commissioning agent, energy manager and researcher) and four core issues (i.e. unclear responsibility, poor collaboration and communication, insufficient knowledge and experience, and lack of information integrity) have intensive relationships. The core stakeholders account for 75% of the total stakeholders, whereas the core issues account for 25% of the total issues. Moreover, five strategies that are useful in promoting collaboration are proposed and validated. After the proposed strategies are implemented, the density of the collaboration network increased from 1.242 to 2.652. This study is not only expected to help researchers in understanding the complex interrelationships between stakeholders and issues but also practitioners in promoting effective collaboration strategies.

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.