Autonomy-oriented proximity mobile social network modeling in smart city for emergency rescue

Nearest neighbor mobile social network means that movers approaching in position communicate through their social sensors, which is called Proximity Mobile Social Network. Proximity Mobile Social Network can provide more social and business opportunities for users. To carry out disaster relief work in post-disaster environment, we need to collect incident information during the search process and report to the sink in time. Proximity Mobile Social Network provides flexible systems for emergency handling and disaster relief. Therefore, how to find a better data forwarding and routing strategy is the key problem of post-disaster rescue, and the research of user mobility model is the basis of the above problems. This article presents an Autonomy-Oriented Proximity Mobile Social Network modeling for emergency rescue in smart city, which simulates the network operating environment. First, we verify the performance of Autonomy-Oriented Proximity Mobile Social Network model in terms of self-organization, scale-free, aggregation, and community structure. Then, the rescue efficiency is discussed through the coverage of mobile sensors. Finally, performance of the routing strategy based on Autonomy-Oriented Proximity Mobile Social Network model is analyzed, and the effectiveness of the method is proved.

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health.

Development of online tool using Social Network Modeling concepts and Quality Control tools for faster product development

Earlier engineering was done by few people. But now with ever increasing knowledge base with the public, there is need to tap that source. But this tapping of knowledge base of the public cannot be done in technical terms. There is need to express knowledge in non technical terms by use of logical reasoning, modeling of ideas, etc. Seeing these non technical details on website, some people can even put their responses, or ask for clarification of some points. Now if designers see this information they can answer those queries. Modern engineering is not limited to the handful designers alone, country has a large knowledge base in public, but a company cannot hire all people, so there needs to place a mechanism by which designers can come in contact with the people, understand and incorporate their feedback and answer their queries.Quality control tools (like flowchart, Cause-effect or fishbone diagrams, force field analysis, interrelations digraph, affinity diagram, and variability matrix) have been quite popular as they explain the engineering processes in a non technical way, yet they analyze the underlying process thoroughly.With the advent of modern computers, a new paradigm has been opened in terms of Social Network analysis, which connects people in a faster manner and lead to quicker information flow. In this project we propose to use the above mentioned Quality Control tools with Social Network modeling concepts to bring every bit of information available with the people at lower working level in a systematic and structured manner in the front of senior decision making level.

Collective Avoidance of Social and Health Venues and HIV Racial Inequities: Network Modeling of Venue Avoidance on Venue Affiliation, Social Networks, and HIV Risk

Many younger Black men who have sex with men (YBMSM) are exposed to homonegativity, societal stigma, and racial discrimination in their social environment. This study uses a social network modeling methodology to identify aspects of the social environment that are not often described, that is, the places and spaces or “venues” where YBMSM socialize or where they receive HIV prevention services. In particular, we identify the structural features of avoidance of these venues as an indicator of negative experiences, using bipartite exponential random graph models. Our study theorizes that YBMSM avoid certain venues en masse through information diffusion among social network members. We specify two social mechanisms of collective venue avoidance—(1) homophily (i.e., ego–alter similarity in venue avoidance) and (2) popular opinion leaders (as early adopters)—and test the corresponding hypotheses that (Hypothesis 1) socially connected individuals avoid venues together and that (Hypothesis 2) popular individuals would be more likely to avoid venues. Based on data collected from YBMSM aged 16 to 29 years between 2014 and 2016 in Houston, Texas ( N = 227) and Chicago, Illinois ( N = 241), results indicate that Hypothesis 1 was supported in both cities but that Hypothesis 2 was supported only in Chicago. The findings suggest that the structural patterns of venue avoidance are different between cities and may inform dissemination of prevention messages and delivery of venue- and social influence–based HIV interventions.

An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.

Fuzzy Social Network Modeling for Influencing Consumer Behavior

Social networks have become an important component in most companies' bag of tools for managing and influencing consumer behavior. It is imperative for modern organizations to fully understand these social networks and have at their disposal an armada of tools to intelligently model and manipulate these complex structures in order to accomplish their goals. In order to most effectively and intelligently use social networks, decision makers and planners must be able to bring to bear their expertise, experience, and professional intuition on issues involving these networks. This requires an understanding, comprehension, and view of social networks that is compatible with their human cognition and perception. They must be able to understand the structure and dynamics of social networks in terms of human-focused concepts. In this chapter, the authors investigate and describe the use of the FISNA technology to help in the modeling of consumer behavior-related concepts in social networks.