Social Influence and Dynamic Network Structure

2018 ◽  
pp. 173-196
Author(s):  
Ye Ouyang ◽  
Mantian Hu ◽  
Alexis Huet ◽  
Zhongyuan Li
Keyword(s):  
Social Influence ◽  
Network Structure ◽  
Dynamic Network

scholarly journals Dynamic network prediction

2020 ◽  
Vol 8 (4) ◽  
pp. 574-595
Author(s):  
Ravi Goyal ◽  
Victor De Gruttola
Keyword(s):  
Social Relationships ◽  
Network Structure ◽  
Broad Class ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Simulation Studies ◽  
Population Composition ◽  
Statistical Framework ◽  
Network Properties ◽  
Network Prediction

AbstractWe present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

A CONNECTIONIST ABM OF SOCIAL CATEGORIZATION PROCESSES

2012 ◽  
Vol 15 (06) ◽  
pp. 1250077 ◽  
Author(s):  
DIRK VAN ROOY
Keyword(s):  
Social Influence ◽  
Network Structure ◽  
Social Adaptation ◽  
Multiple Sources ◽  
Agent Based ◽  
Individual Level ◽  
Extensive Research Program ◽  
Micro Level ◽  
The Individual ◽  
Over Time

This paper introduces a connectionist Agent-Based Model (cABM) that incorporates detailed, micro-level understanding of social influence processes derived from laboratory studies and that aims to contextualize these processes in such a way that it becomes possible to model multidirectional, dynamic influences in extended social networks. At the micro-level, agent processes are simulated by recurrent auto-associative networks, an architecture that has a proven ability to simulate a variety of individual psychological and memory processes [D. Van Rooy, F. Van Overwalle, T. Vanhoomissen, C. Labiouse and R. French, Psychol. Rev. 110, 536 (2003)]. At the macro-level, these individual networks are combined into a "community of networks" so that they can exchange their individual information with each other by transmitting information on the same concepts from one net to another. This essentially creates a network structure that reflects a social system in which (a collection of) nodes represent individual agents and the links between agents the mutual social influences that connect them [B. Hazlehurst, and E. Hutchins, Lang. Cogn. Process. 13, 373 (1998)]. The network structure itself is dynamic and shaped by the interactions between the individual agents through simple processes of social adaptation. Through simulations, the cABM generates a number of novel predictions that broadly address three main issues: (1) the consequences of the interaction between multiple sources and targets of social influence (2) the dynamic development of social influence over time and (3) collective and individual opinion trajectories over time. Some of the predictions regarding individual level processes have been tested and confirmed in laboratory experiments. In a extensive research program, data is currently being collected from real groups that will allow validating the predictions of cABM regarding aggregate outcomes.

scholarly journals Modelling the dispersion of SARS-CoV-2 on a dynamic network graph

2020 ◽  
Author(s):  
Patrick Bryant ◽  
Arne Elofsson
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Preferential Attachment ◽  
Age Groups ◽  
Dynamic Network ◽  
Social Contacts ◽  
Social Network Structure ◽  
The Social ◽  
Epidemic Development ◽  
The Impact

AbstractBackgroundWhen modelling the dispersion of an epidemic using R0, one only considers the average number of individuals each infected individual will infect. However, we know from extensive studies of social networks that there is significant variation in the number of connections and thus social contacts each individual has. Individuals with more social contacts are more likely to attract and spread infection. These individuals are likely the drivers of the epidemic, so-called superspreaders. When many superspreaders are immune, it becomes more difficult for the disease to spread, as the connectedness of the social network dramatically decreases. If one assumes all individuals being equally connected and thus as likely to spread disease as in a SIR model, this is not true.MethodsTo account for the impact of social network structure on epidemic development, we model the dispersion of SARS-CoV-2 on a dynamic preferential attachment graph which changes appearance proportional to observed mobility changes. We sample a serial interval distribution that determines the probability of dispersion for all infected nodes each day. We model the dispersion in different age groups using age-specific infection fatality rates. We vary the infection probabilities in different age groups and analyse the outcome.ResultsThe impact of movement on network dynamics plays a crucial role in the spread of infections. We find that higher movement results in higher spread due to an increased probability of new connections being made within a social network. We show that saturation in the dispersion can be reached much earlier on a preferential attachment graph compared to spread on a random graph, which is more similar to estimations using R0.ConclusionsWe provide a novel method for modelling epidemics by using a dynamic network structure related to observed mobility changes. The social network structure plays a crucial role in epidemic development, something that is often overlooked.

Maleated trans -1,4-polyisoprene from Eucommia ulmoides Oliver with dynamic network structure and its shape memory property

Polymer ◽  
2014 ◽  
Vol 55 (25) ◽  
pp. 6488-6493 ◽  
Author(s):  
Takashi Tsujimoto ◽  
Kenichi Toshimitsu ◽  
Hiroshi Uyama ◽  
Shinya Takeno ◽  
Yoshihisa Nakazawa
Keyword(s):  
Shape Memory ◽  
Network Structure ◽  
Dynamic Network ◽  
Eucommia Ulmoides ◽  
Shape Memory Property ◽  
Memory Property ◽  
Eucommia Ulmoides Oliver

scholarly journals Dynamic network structure identification with prediction error methods - basic examples

2012 ◽  
Vol 45 (16) ◽  
pp. 876-881 ◽  
Author(s):  
Arne G. Dankers ◽  
Paul M.J. Van den Hof ◽  
Peter S.C. Heuberger ◽  
Xavier Bombois
Keyword(s):  
Network Structure ◽  
Prediction Error ◽  
Dynamic Network ◽  
Structure Identification ◽  
Prediction Error Methods

scholarly journals When social influence promotes the wisdom of crowds

2020 ◽  
Author(s):  
Abdullah Almaatouq ◽  
M. Amin Rahimian ◽  
Abdulla Alhajri
Keyword(s):  
Social Influence ◽  
Network Structure ◽  
Human Subjects ◽  
Wisdom Of Crowds ◽  
Social Network Structure ◽  
Crowd Wisdom ◽  
The Social ◽  
Computational Sciences ◽  
The Impact ◽  
The Wisdom Of Crowds

Whether, and under what conditions, groups exhibit "crowd wisdom" has been a major focus of research across the social and computational sciences. Much of this work has focused on the role of social influence in promoting the wisdom of the crowd versus leading the crowd astray, resulting in conflicting conclusions about how the social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 4,002 human subjects), we find that the wisdom of crowds critically depends on the interaction between (i) the centralization of the social influence network and (ii) the distribution of the initial, individual estimates, i.e., the estimation context. Specifically, we propose a feature of the estimation context that measures the suitability of the crowd to benefit from influence centralization and show its significant predictive powers empirically. By adopting a framework that integrates both the structure of the social influence and the estimation context, we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds.

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