Impact Of Social Network Structure Research Articles

Predicting urban innovation from the US Workforce Mobility Network

While great emphasis has been placed on the role of social interactions as a driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among metropolitan areas in the US. We found that node centrality computed on this network accounts for most of the variability observed in cities’ innovation performance and significantly outperforms other predictors such as population size or density, suggesting that policies and initiatives aiming at sustaining innovation processes might benefit from fostering professional networks alongside other economic or systemic incentives. As opposed to previous approaches powered by census data, our model can be updated in real-time upon open databases, opening up new opportunities both for researchers in a variety of disciplines to study urban economies in new ways, and for practitioners to design tools for monitoring such economies in real-time.

Impact of Social Network Structures on Uncertain Opinion Formation

When people express their opinions about a certain issue, they often give uncertain opinions rather than exact opinions. Particularly, these uncertain opinions will evolve in social networks. Therefore, in this paper, we focus on investigating uncertain opinion formation with social networks under bounded confidence. Specifically, we define the uncertain opinions by numerical interval opinions, whose ranges are between zero and one, and the larger width of numerical interval opinions means the more uncertainty of the opinions. Meanwhile, we describe social network structures by ER random graphs with different agents’ scales and network connected probabilities. Then, we present the detailed simulation experiments to reveal the strong impact of social network structures on uncertain opinion formation. Simulation results show that: 1) larger agents’ scales will yield the smaller ratios of agents expressing the uncertain opinions and larger average widths of uncertain opinions; 2) the average stable time starts increasing and then decreases with the increase in the network connected probabilities; and 3) larger network connected probabilities will yield less opinion clusters and the smaller ratios of the extremely small clusters in all clusters. The obtained results are helpful for the government and public opinion management departments to understand and manage uncertain public opinion evolution effectively.

Assessing the Roles of Network Structure and Social Media in Inter-units Knowledge Sharing

Sharing knowledge across different units within an organization can be considered as one of the organization’s fundamental operation. Nonetheless, sharing information and knowledge across boundaries presents tremendous challenges for organization. This paper discusses the impact of social network structure and social media in mediating knowledge sharing against unit task performance. Based on the review of extant literatures in intra-organization information and knowlegde sharing, this paper accentuates on three propositions as follows: a) the use of online collaborative media reduces time spend on task by accelerating the electronic document quality assurance process, b) integrating expert ability with the use of collaborative media reduces time spend on task by accelerating the electronic document quality assurance process, and c) social network structure increase the likelihood to extract personal advice from colleagues, thus improve unit’s work quality.

Social network structure and the trade-off between social utility and economic performance

We put forward a computational multi-agent model capturing the impact of social network structure on individuals’ social trust, willingness to cooperate, social utility and economic performance. Social network structure is modeled as four distinct social capital dimensions: degree, centrality, bridging and bonding social capital. Model setup draws from socio-economic theory and empirical findings based on our novel survey dataset. Results include aggregate-level comparative statics and individual-level correlations. We find, inter alia, that societies that either are better connected, exhibit a lower frequency of local cliques, or have a smaller share of family-based cliques, record relatively better aggregate economic performance. As long as family ties are sufficiently valuable, there is a trade-off between aggregate social utility and economic performance, and small world networks are then socially optimal. We also find that in dense networks and trustful societies, there is a trade-off between individual social utility and economic performance; otherwise both outcomes are positively correlated in the cross section.

A new prediction model of infectious diseases with vaccination strategies based on evolutionary game theory

Infectious diseases have proven to be remarkably resilient foes of human health and so the prevention and control of infectious diseases have been attracting the attention of all countries over the world. Vaccination is an effective way to prevent the spread of infectious diseases. However, vaccination is a long-standing social dilemmas due to the vaccine’s risk by itself and the spread of infectious diseases in the population depends on not only the pathogen itself, but also the impact of social network structures. In this paper, we propose a new prediction model of infectious diseases with new vaccination strategies based on network structures and dynamic replicator. In our model, we consider not only the subsidies of vaccine failure but also the incentive strategy for medical treatment to promote individuals to take the initiative to vaccinate. At the same time, in decision-making phase, we use weighted average benefits of all participants to update their strategies due to individual difference. Simulation experiments show that the our proposed model is much effective and better than other existing models. We also use Jacobian matrix to prove the stability of dynamic equilibrium for our proposed model.

Social network structures and bank runs

This paper investigates the impact of social network structures of depositors on bank runs. The analyzed network structures include random networks, small-world networks and scale-free networks. Simulation results show that the probability of bank run occurrence in random networks is larger than that in small-world networks, but the probability of bank run occurrence in scale-free networks drops from the highest to the lowest among the three types of network structures with the increase of the proportion of impatient depositors. The average degree of depositor networks has a significant impact on bank runs, but this impact is related to the proportion of impatient depositors and the confidence levels of depositors in banks.

Networks and Transaction Costs

Based on the well-known fact that social networks can provide effective mechanisms that help to increase the trust level between two trade partners, we apply a simple game-theoretical framework to derive transaction costs as a high risk of opportunistic behavior in a repeated trade relation determined by the density and size of trading networks. In the empirical part of the paper we apply a two stage procedure to estimate the impact of social network structures on farm’s transaction costs observed for different input and output markets. At a first stage we estimate a multiple input-multiple output stochastic Ray production function to generate relative shadow prices of three inputs and two outputs traded by farms. At a second stage a structural equation system is derived from the first order conditions of farm’s profit maximization to estimate simultaneously the of commodity specific transaction cost functions for all traded farm inputs and outputs. Estimation results based on a sample of 315 Polish farms imply a significant influence of social network structures on farm’s transaction costs. Moreover, estimated transaction costs correspond to a reasonable amount of farm specific shadow prices.

Ties with Potential: Social Network Structure and Innovative Climate in Dutch Schools

Background/ContextSimilar to the United States, government efforts to improve education in the Netherlands are focused on innovation and the development of collaborative structures to support the generation of new knowledge. However, empirical evidence of the relationship between social linkages and innovation in education is scarce.ObjectiveThe aim of the study was to examine the impact of social network structure on schools’ innovative climate, as mediated by teachers’ involvement in decision-making.SettingThis article reports on a study among 775 educators in 53 elementary schools in a large educational system in the Netherlands.Research DesignA quantitative survey using Likert-type scales and social network questions on work-related and personal advice was analyzed using social network analysis and multiple regression analyses.Conclusions/RecommendationsFindings indicated that the more densely connected teachers were in regard to work-related and personal advice, the more they perceived their schools’ climate to be supportive of innovation. Highly dense work-related network structures also typified teams that perceived strong teacher involvement in decision-making. Moreover, results suggested that the positive relationship between density of work-related advice networks and innovation-supportive school climate could be partially explained by increased shared decision-making. Implications of the study for teachers, organizations, leadership, and policy are discussed.

Information Flow within the Social Network Structure of a Joint Strategic Plan for Management of Great Lakes Fisheries

AbstractTransboundary fish stocks are governed by multiple entities, involving individuals with different expertise and sociocultural backgrounds and representing various institutions and jurisdictions. At times, individuals from these entities collaboratively make fishery governance decisions, and the existence of collegial or personal relationships may facilitate the decision‐making process and result in better management of fish stocks. Although studies have assessed several aspects of fisheries institutional structures, very few have looked at the impact of social network structure. In this study, we found evidence for the perceived effectiveness of A Joint Strategic Plan for Management of Great Lakes Fisheries in its social network structure. We focused on the frequency of interactions for exchanging information about Great Lakes fish stocks in general and lake sturgeon Acipenser fulvescens in particular. These informational exchanges correspond to distinct social network structures, the fish stocks network and the lake sturgeon network. Similarities in participants’ attributes and perceptions about fisheries governance may have facilitated the formation of social ties within these networks. The lake sturgeon network had significant subgroups wherein flow was more concentrated. The formation of these subgroups appears to have been influenced by individuals’ employer type, committee affiliation, and perception about lake sturgeon governance. These subgroups were found to have 11 central individuals and two central subgroups. When comparing the central and noncentral individuals’ attributes and perceptions, we did not find a significant difference that explains why the 11 individuals were central. Similarly, no significant differences were found in comparing the central and noncentral subgroups. In assessing the flow of information within and across these subgroups, we found that it was evenly distributed with some specialization of flow between subgroups, the latter probably related to the presence of bridging individuals connecting the subgroups.Received March 24, 2010; accepted March 23, 2011

Network Structures and Online Technology Adoption

How do social network structures influence online technology adoption? As few studies have examined the impact of social network structures on technology adoption, especially with regard to online teams deciding what technology to adopt, the answer to this question has remained elusive. In this research, we argue that comembership is one of the most important mechanisms through which online social networks are built and online network structures emerge. We further test the magnitude of the influence of network structures on technology adoption in virtual workspaces. We focus on three key network structure variables: network centrality, network closure, and network brokerage. We argue that network centrality and network brokerage are positively related to online technology adoption, yet network closure has the opposite impact. To illustrate our theory and test the hypotheses, we build online social networks using open-source software development data and examine the adoption of the latest software version control technology. Methodologically, we address the heterogeneity and simultaneity issues between network structures and adoption behavior; thus, we show a compelling relationship between network structures and online technology adoption.

Impact of Social Network Structure on Multimedia Fingerprinting Misbehavior Detection and Identification

Users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems. In video-sharing social networks, users have access to extra resources from their peers, and they also contribute their own resources to help others. Each user wants to maximize his/her own payoff, and they negotiate with each other to achieve fairness and address this conflict. However, some selfish users may cheat to their peers and manipulate the system to maximize their own payoffs, and cheat prevention is a critical requirement in many social networks to stimulate user cooperation. It is of ample importance to design monitoring mechanisms to detect and identify misbehaving users, and to design cheat-proof cooperation stimulation strategies. Using video fingerprinting as an example, this paper analyzes the complex dynamics among colluders during multiuser collusion, and explores possible monitoring mechanisms to detect and identify misbehaving colluders in multiuser collusion. We consider two types of colluder networks: one has a centralized structure with a trusted ringleader, and the other is a distributed peer-structured network. We investigate the impact of network structures on misbehavior detection and identification, propose different selfish colluder identification schemes for different colluder networks, and analyze their performance. We show that the proposed schemes can accurately identify selfish colluders without falsely accusing others even under attacks. We also evaluate their robustness against framing attacks and quantify the maximum number of framing colluders that they can resist.

The impact of network clustering and assortativity on epidemic behaviour

Epidemic models have successfully included many aspects of the complex contact structure apparent in real-world populations. However, it is difficult to accommodate variations in the number of contacts, clustering coefficient and assortativity. Investigations of the relationship between these properties and epidemic behaviour have led to inconsistent conclusions and have not accounted for their interrelationship. In this study, simulation is used to estimate the impact of social network structure on the probability of an SIR (susceptible-infective-removed) epidemic occurring and, if it does, the final size. Increases in assortativity and clustering coefficient are associated with smaller epidemics and the impact is cumulative. Derived values of the basic reproduction ratio ( R 0 ) over networks with the highest property values are more than 20% lower than those derived from simulations with zero values of these network properties.

Consensual Decision-Making Among Epistemic Peers

ABSTRACTThis paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their stability with the help of numerical simulations.

Consensual Decision-Making among Epistemic Peers

This paper focuses on the question of how to resolve disagreement, and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the equal weight view to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their stability with the help of numerical simulations.