scholarly journals Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles

2020 ◽  
Vol 12 (4) ◽  
pp. 69 ◽  
Author(s):  
Kashif Zia ◽  
Muhammad Shafi ◽  
Umar Farooq
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Internet Of Vehicles ◽  
Agent Based ◽  
Smart Vehicles ◽  
Multi Scale ◽  
The Social ◽  
Simulation Results ◽  
Connected Person ◽  
The Internet Of Things

The latest manifestation of “all connected world" is the Internet of Things (IoT), and Internet of Vehicles (IoV) is one of the key examples of IoT these days. In Social IoV (SIoV), each vehicle is treated as a social object where it establishes and manages its own Social Network (SN). Incidentally, most of the SIoV research in the literature is related to proximity-based connectivity and interactions. In this paper, we bring people in the loop by incorporating their SNs. While emphasizing a recommendation scenario, in which vehicles may require recommendations from SNs of their owners (in addition to their own SIoV), we proposed an agent-based model of information sharing (for context-based recommendations) on a hypothetical population of smart vehicles. Some important hypotheses were tested using a realistic simulation setting. The simulation results reveal that a recommendation using weak ties is more valuable than a recommendation using strong ties in pure SIoV. The simulation results also demonstrate that recommendations using the most-connected person in the social network are not more valuable than recommendation using a random person in the social network. The model presented in this paper can be used to design a multi-scale recommendation system, which uses SIoV and a typical SN in combination.

scholarly journals Towards Exploration of Social in Social Internet of Vehicles Using an Agent-Based Simulation

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Kashif Zia ◽  
Arshad Muhammad ◽  
Abbas Khalid ◽  
Ahmad Din ◽  
Alois Ferscha
Keyword(s):  
Context Awareness ◽  
Data Science ◽  
Social Ties ◽  
Social Aspects ◽  
Internet Of Vehicles ◽  
Agent Based ◽  
Smart Vehicles ◽  
The Social ◽  
Simulation Results ◽  
Data Constraints

Internet of Vehicles (IoV) is turning out to be one of the first impressive examples of Internet of Things (IoT). In IoV, the factors of connectivity and interaction/information dispersion are equally important as sensing/actuating, context-awareness, services provisioning, etc. However, most of the researches related to connectivity and interaction are constrained to physics of signaling and data science (semantics/contents), respectively. Very rapidly, the meanings of these factors are changing due to evolution of technologies from physical to social domain. For example, Social IoV (SIoV) is a term used to represent when vehicles build and manage their own social network. Hence, in addition to physical aspects, the social aspects of connectivity and information dispersion towards these systems of future should also be researched, a domain so far ignored in this particular context. In this paper, an agent-based model of information sharing (for context-based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts the dispersion of novel information (necessary for a recommender system) substantially. It is also observed that as the network evolves due to incremental interactions, the recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.

scholarly journals Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Shelley D. Dionne ◽  
Hiroki Sayama ◽  
Francis J. Yammarino
Keyword(s):  
Decision Making ◽  
Social Network ◽  
Network Structure ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Theoretic Approach ◽  
Agent Based ◽  
Social Network Structure ◽  
Agent Based Simulations ◽  
Simulation Results

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theoretic approach to collective decision making, agent-based simulations were conducted to investigate how human collective decision making would be affected by the agents’ diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing nontrivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multilevel decision making are discussed.

scholarly journals Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study (Preprint)

2018 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Bojan Simoski ◽  
Eric Fernandes de Mello Araújo ◽  
Kirsten E Bevelander ◽  
William J Burk ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Selection Criteria ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Network Characteristics ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention

BACKGROUND Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. CONCLUSIONS Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

scholarly journals Recommendation system based on user actions in the social network

2020 ◽  
Vol 32 (3) ◽  
pp. 101-108
Author(s):  
Vitaly Viktorovich MONASTYREV ◽  
Pavel Dmitrievich DROBINTSEV
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
The Social ◽  
User Actions

scholarly journals On the effectiveness of multi-feature evacuation systems: an agent-based exploratory simulation study

2021 ◽  
Vol 7 ◽  
pp. e531
Author(s):  
Kashif Zia ◽  
Umar Farooq ◽  
Muhammad Shafi ◽  
Alois Ferscha
Keyword(s):  
Comparative Analysis ◽  
Social Context ◽  
Social Influence ◽  
Evacuation Modeling ◽  
Agent Based ◽  
Unified Modeling ◽  
Evacuation Strategy ◽  
The Social ◽  
Simulation Results ◽  
Real Challenge

Evacuation modeling and simulation are usually used to explore different possibilities for evacuation, however, it is a real challenge to integrate different categories of characteristics in unified modeling space. In this paper, we propose an agent-based model of an evacuating crowd so that a comparative analysis of a different sets of parameters categorized as individual, social and technological aspects, is made possible. In particular, we focus on the question of rationality vs. emotionalism of individuals in a localized social context. In addition to that, we propose and model the concept of extended social influence, thereby embedding technological influence within the social influence, and analyze its impact on the efficiency of evacuation. NetLogo is used for simulating different variations in environments, evacuation strategies, and agents demographics. Simulation results revealed that there is no substantial advantage of informational overload on people, as this might work only in those situations, where there are fewer chances of herding. In more serious situations, people should be left alone to decide. They, however, could be trained in drills, to avoid panicking in such situations and concentrate on making their decisions solely based on the dynamics of their surroundings. It was also learned that distant connectivity has no apparent advantage and can be ruled out while designing an evacuation strategy based on these recommendations.

Research on Spreading and Evolution of Opinion in Online Social Network

2013 ◽  
Vol 760-762 ◽  
pp. 1982-1986
Author(s):  
Chang Lun Zhang ◽  
Chao Li
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
External Factor ◽  
Online Social Network ◽  
Important Impact ◽  
The Social ◽  
Simulation Results ◽  
Opinion Evolution

the online social network has served as a critical medium for information dissemination, diffusion of epidemics and spread of behavior. In this paper, we proposed a model of opinion spreading and evolution based on online social network, where the social temperature is taken into account. First, the forms and features of the opinion spreading and evolution in social network are analyzed. Then a model of opinion spreading and evolution is established, in which social temperature as an external factor participate the opinion evolution of nodes and then affect the opinion spreading. Simulation results show that social temperature has an important impact on the opinion spreading and evolution.

Cold Start Problem Alleviation in a Research Paper Recommendation System Using the Random Walk Approach on a Heterogeneous User-Paper Graph

2020 ◽  
Vol 16 (2) ◽  
pp. 24-48
Author(s):  
Manju G. ◽  
Abhinaya P. ◽  
Hemalatha M.R. ◽  
Manju Ganesh G. ◽  
Manju G.G.
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Interaction ◽  
Recommendation System ◽  
Cold Start ◽  
Research Paper ◽  
Novel Approach ◽  
Interaction Factor ◽  
The Social ◽  
Cold Starts

Recommendation approaches generally fail to recommend newly-published papers as relevant, owing to the lack of prior information about the said papers and, more particularly, problems associated with cold starts. It would appear, to all intents and purposes, that researchers currently interact more on social networks than they normally would in academic circles, and relationships of a purely academic nature have witnessed a paradigm shift, in keeping with this new trend. In existing paper recommendation methods, the social interaction factor has yet to play a pivotal role. The authors propose a social network-based research paper recommendation method, that alleviates cold start problems by incorporating users' social interaction, as well as topical relevancy, among assorted papers in the Mendeley academic social network using a novel approach, random walk Ergodic Markov Chain. The system yields improved results after cold start alleviation, compared with the existing system.

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