Diffusion in Networks

2016 ◽  
pp. 478-503 ◽  
Author(s):  
P. J. Lamberson
Keyword(s):  
Social Learning ◽  
Network Structure ◽  
Stochastic Dominance ◽  
Degree Distribution ◽  
Future Research ◽  
Threshold Models ◽  
Epidemiological Models ◽  
Spreading Process ◽  
Dominance Relations ◽  
Diffusion In Networks

This chapter examines models of diffusion in networks, and specifically how the topology of the network impacts the spreading process. The chapter begins by discussing epidemiological models and how stochastic dominance relations can be used to understand the effect of the degree distribution of the network. The chapter then turns to more sophisticated models of social influence, including threshold models and models of social learning. A key insight that emerges from the collection of models discussed is that not only does network structure matter, but how the network matters depends on the way in which agents influence one another. Network features that facilitate contagion under one model of influence can inhibit diffusion in another. The chapter concludes with thoughts on directions for future research.

scholarly journals Social Learning in Social Networks

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
PJ Lamberson
Keyword(s):  
Social Learning ◽  
Stochastic Dominance ◽  
Degree Distribution ◽  
New Technology ◽  
Field Approximation ◽  
Mean Field Approximation ◽  
Model Parameters ◽  
Prior Beliefs ◽  
Order Stochastic Dominance ◽  
The Relationship

This paper analyzes a model of social learning in a social network. Agents decide whether or not to adopt a new technology with unknown payoffs based on their prior beliefs and the experiences of their neighbors in the network. Using a mean-field approximation, we prove that the diffusion process always has at least one stable equilibrium, and we examine the dependence of the set of equilibria on the model parameters and the structure of the network. In particular, we show how first and second order stochastic dominance shifts in the degree distribution of the network impact diffusion. We find that the relationship between equilibrium diffusion levels and network structure depends on the distribution of payoffs to adoption and the distribution of agents' prior beliefs regarding those payoffs, and we derive the precise conditions characterizing those relationships. For example, in contrast to contagion models of diffusion, we find that a first order stochastic dominance shift in the degree distribution can either increase or decrease equilibrium diffusion levels depending on the relationship between agents' prior beliefs and the payoffs to adoption. Surprisingly, adding more links can decrease diffusion even when payoffs from the new technology exceed those of the status quo in expectation.

scholarly journals Sick and edgy: walk-counting as a metric of epidemic spreading on networks

2008 ◽  
Vol 6 (39) ◽  
pp. 897-907 ◽  
Author(s):  
Dennis C. Wylie ◽  
Wayne M. Getz
Keyword(s):  
Structural Properties ◽  
Network Structure ◽  
Adjacency Matrix ◽  
Degree Distribution ◽  
Structured Populations ◽  
Epidemic Spread ◽  
Epidemic Spreading ◽  
Spreading Process ◽  
Dynamic Pattern ◽  
General Network

A network structure metric is herein suggested for the investigation of the behaviour of epidemic spreading processes in general network-structured populations. This simple measure, based on the algebraic powers of the adjacency matrix associated with the network in question, is shown to admit a heuristic interpretation as a representation of a spreading process similar to standard epidemic models. It is further shown that the values of this metric may be of use in understanding the dynamic pattern of epidemic spread on networks of greatly varying structural properties (e.g. the degree distribution, the assortativity/dissortativity and the clustering).

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Social learning and networks in Nestor parrots

2021 ◽  
Author(s):  
◽  
Daniel Donoghue
Keyword(s):  
Social Network ◽  
Social Learning ◽  
Tool Use ◽  
Social Information ◽  
Habitat Modification ◽  
Future Research ◽  
Video Playback ◽  
Food Aversion ◽  
Network Analyses ◽  
The Social

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>

scholarly journals Social learning and networks in Nestor parrots

2021 ◽  
Author(s):  
Daniel Donoghue
Keyword(s):  
Social Network ◽  
Social Learning ◽  
Tool Use ◽  
Social Information ◽  
Habitat Modification ◽  
Future Research ◽  
Video Playback ◽  
Food Aversion ◽  
Network Analyses ◽  
The Social

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>

Divergent Systems of Agonistic Behaviour in Lemurid Primates

Behaviour ◽  
1997 ◽  
Vol 134 (3-4) ◽  
pp. 225-274 ◽  
Author(s):  
Michael E. Pereira ◽  
Peter M. Kappeler
Keyword(s):  
Social Systems ◽  
Study Groups ◽  
Third Party ◽  
Future Research ◽  
Agonistic Interaction ◽  
Agonistic Behaviour ◽  
Adult Males ◽  
Dominance Relations ◽  
Post Conflict ◽  
Ringtailed Lemurs

AbstractTwo semifree-ranging groups of ringtailed lemurs (Lemur catta) and two co-ranging groups of redfronted lemurs (Eulemur fulvus rufus) were studied across a two-year period to characterise and contrast the adult agonistic behaviour these primates exhibit within groups. Temporal analyses of behavioural data distinguished agonistic from non-agonistic behaviour and aggressive from submissive behaviour. The ringtailed lemurs employed a diverse repertoire of behavioural elements to communicate agonistic intent. More than 50% of these elements were signals and nearly 50% of signals were submissive. The agonistic repertoire of the redfronted lemurs, by contrast, was relatively unelaborated: less than 40% of agonistic behaviour in this species comprised signals and less than 20% of signals were submissive. These structural differences underlay marked species differences in agonistic interaction and relationship. All pairs of ringtailed lemurs maintained dominance relations resembling those seen in many anthropoid primates: subordinates consistently signalled submissively to dominant partners, often in the absence of aggression. Dominance relations among members of each sex were seasonally unstable and not always transitive (hierarchical) during periods of stability, however. Redfronted lemurs, by contrast, did not maintain dominance relations, failing to respond agonistically to most aggression received (52% of interactions) and responding with aggression on many other occasions (12%). Even applying relaxed criteria, few adult redfronted dyads (14%) showed consistent asymmetries in agonistic relations and several never exhibited any asymmetry. Lacking dominance, E. f rufus relied heavily on alternate behavioural mechanisms to moderate social conflict as frequent and intense as that seen in study groups of ringtailed lemurs. These included a great inclination not to respond agonistically to aggression, a distinctive behavioural proposal to limit or terminate dyadic conflict (Look away), post-conflict reconciliation, and relatively frequent third-party aggression. The existence of such divergent systems of agonistic behaviour in partially sympatric, closely related and generally similar prosimian primates offers important opportunities for comparative study of the ecology, development, and evolution of mammalian social systems. Future research may reveal ecophysiological factors that promote the use of dominance behaviour among like-sexed ringtailed lemurs and show how the relative absence of dominance relates to other major elements of redfronted lemur biology, including 'special relationships' of variable duration between adult males and females.

Adult Learner Characteristics Important for Successful Learning in Cross-Cultural Web-Based Learning Environments in Higher Education

2021 ◽  
pp. 436-469
Author(s):  
Joseph George M. Lutta
Keyword(s):  
Higher Education ◽  
Social Learning ◽  
Learning Environments ◽  
Teaching And Learning ◽  
Individual Characteristics ◽  
Cross Cultural ◽  
Future Research ◽  
Web Based ◽  
Research And Practice ◽  
Web Based Learning

For more than 40 years, cognitive psychological perspectives have dominated pedagogical frameworks and models for designing technology-mediated teaching and learning environments. Social learning perspectives are increasingly becoming viable or even desirable frames for research and practice as pertains to teaching and learning, particularly in web-based learning environments (WBLEs). The author considers these social learning perspectives and how they relate to the design and implementation of curricula that are delivered in web-based learning environments in higher education. The author further reviews the foundational theories of adult learning that enhance adult learners' experiences in cross-cultural web-based learning environments. This review and analysis of the research related to social learning perspectives on WBLEs have three implications for future research and practice: (1) examining learners' individual characteristics in WBLEs, (2) identifying strategies for promoting social interaction in WBLEs, and (3) developing effective design principles for WBLEs. The author presents recommendations for future research.

The interplay between gamification and network structure in social learning environments: A case study

2020 ◽  
Vol 28 (4) ◽  
pp. 814-836
Author(s):  
María E. Sousa‐Vieira ◽  
Jose C. López‐Ardao ◽  
Manuel Fernández‐Veiga ◽  
Orlando Ferreira‐Pires
Keyword(s):  
Social Learning ◽  
Learning Environments ◽  
Network Structure

scholarly journals Predictive accuracy of a hierarchical logistic model of cumulative SARS-CoV-2 case growth until May 2020

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Levente Kriston
Keyword(s):  
Disease Transmission ◽  
Logistic Model ◽  
Predictive Accuracy ◽  
Future Research ◽  
Cumulative Number ◽  
Epidemiological Models ◽  
First Case ◽  
Distribution Of Resources ◽  
The One ◽  
Models Of Disease

Abstract Background Infectious disease predictions models, including virtually all epidemiological models describing the spread of the SARS-CoV-2 pandemic, are rarely evaluated empirically. The aim of the present study was to investigate the predictive accuracy of a prognostic model for forecasting the development of the cumulative number of reported SARS-CoV-2 cases in countries and administrative regions worldwide until the end of May 2020. Methods The cumulative number of reported SARS-CoV-2 cases was forecasted in 251 regions with a horizon of two weeks, one month, and two months using a hierarchical logistic model at the end of March 2020. Forecasts were compared to actual observations by using a series of evaluation metrics. Results On average, predictive accuracy was very high in nearly all regions at the two weeks forecast, high in most regions at the one month forecast, and notable in the majority of the regions at the two months forecast. Higher accuracy was associated with the availability of more data for estimation and with a more pronounced cumulative case growth from the first case to the date of estimation. In some strongly affected regions, cumulative case counts were considerably underestimated. Conclusions With keeping its limitations in mind, the investigated model may be used for the preparation and distribution of resources during the initial phase of epidemics. Future research should primarily address the model’s assumptions and its scope of applicability. In addition, establishing a relationship with known mechanisms and traditional epidemiological models of disease transmission would be desirable.

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