scholarly journals An analytical framework for quantifying and testing patterns of temporal dynamics in social networks

2013 ◽  
Vol 85 (1) ◽  
pp. 83-96 ◽  
Author(s):  
Elizabeth A. Hobson ◽  
Michael L. Avery ◽  
Timothy F. Wright
Keyword(s):  
Social Networks ◽  
Temporal Dynamics ◽  
Analytical Framework ◽  
Testing Patterns

Using Communities of Inquiry Online to Perform Tasks of Higher Order Learning

2018 ◽  
pp. 3976-3987 ◽  
Author(s):  
Ramon Tirado-Morueta ◽  
Pablo Maraver-López ◽  
Ángel Hernando-Gómez
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Social Network ◽  
Community Of Inquiry ◽  
Group Structure ◽  
Analytical Framework ◽  
Learning Task ◽  
Cognitive Presence ◽  
Discussion Forums ◽  
Higher Order Learning

In this research the community of inquiry model is used as an analytical framework, along with quantitative content analysis and social network analysis, in order to understand how social and cognitive presence and group structure are affected by type of learning task and social networks. Discussion forums were employed focusing on three types of tasks: analyzing, evaluating, and creating. Over a period of three academic years, a total of 96 discussion forums were analyzed. Results show how social and cognitive presence, are affected by social group structure and centrality of coordinators, depending of type of learning task.

Using Communities of Inquiry Online to Perform Tasks of Higher Order Learning

2019 ◽  
pp. 716-728
Author(s):  
Ramon Tirado-Morueta ◽  
Pablo Maraver-López ◽  
Ángel Hernando-Gómez
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Social Network ◽  
Community Of Inquiry ◽  
Group Structure ◽  
Analytical Framework ◽  
Learning Task ◽  
Cognitive Presence ◽  
Discussion Forums ◽  
Higher Order Learning

In this chapter, the community of inquiry model is used as an analytical framework, along with quantitative content analysis and social network analysis, in order to understand how social and cognitive presence and group structure are affected by the type of learning task and social networks. Discussion forums were employed focusing on three types of tasks: analyzing, evaluating, and creating. Over a period of three academic years, a total of 96 discussion forums were analyzed. Results show how social and cognitive presence are affected by social group structure and centrality of coordinators, depending of type of learning task.

An Activity Theoretical Approach to Examining Virtual Professional Development and Informal Learning via Social Networks

2012 ◽  
pp. 60-74
Author(s):  
Joseph M. Terantino
Keyword(s):  
Professional Development ◽  
Social Networks ◽  
Social Network ◽  
Informal Learning ◽  
Activity Theory ◽  
Analytical Framework ◽  
Learning Processes ◽  
Future Research ◽  
Discussion Forums ◽  
Key Points

This chapter discusses the adoption of activity theory (Engeström, 1987, 2001; Leont’ev, 1978, 1981) as a conceptual framework for analyzing learning processes related to professional development and informal learning via social network environments. The discussion includes an overview of professional development and informal learning via social networks, which highlights the need for a related analytical framework. Activity theory is then described and applied to an example of professional development. This operationalization of activity theory demonstrates the ability of the framework to enable viewing and analyzing learning via social networks such as Facebook communities, wiki and blog spaces, listservs, and discussion forums. The chapter ends with several key points related to implementing activity theory as a solution to investigating behaviors in social networks and potential directions for future research.

scholarly journals An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight

2018 ◽  
Vol 15 (9) ◽  
pp. 2022 ◽  
Author(s):  
Jue Wang ◽  
Mei-Po Kwan
Keyword(s):  
Food Environment ◽  
Temporal Dynamics ◽  
Binary Logistic Regression ◽  
Environmental Exposures ◽  
Analytical Framework ◽  
Environmental Context ◽  
Positioning Systems ◽  
Daily Movement ◽  
Wide Range ◽  
The Relationship

In past studies, individual environmental exposures were largely measured in a static manner. In this study, we develop and implement an analytical framework that dynamically represents environmental context (the environmental context cube) and effectively integrates individual daily movement (individual space-time tunnel) for accurately deriving individual environmental exposures (the environmental context exposure index). The framework is applied to examine the relationship between food environment exposures and the overweight status of 46 participants using data collected with global positioning systems (GPS) in Columbus, Ohio, and binary logistic regression models. The results indicate that the proposed framework generates more reliable measurements of individual food environment exposures when compared to other widely used methods. Taking into account the complex spatial and temporal dynamics of individual environmental exposures, the proposed framework also helps to mitigate the uncertain geographic context problem (UGCoP). It can be used in other environmental health studies concerning environmental influences on a wide range of health behaviors and outcomes.

scholarly journals Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 149676-149705
Author(s):  
Adeel Ahmed ◽  
Khalid Saleem ◽  
Umer Rashid ◽  
Abdullah Baz
Keyword(s):  
Social Networks ◽  
Temporal Dynamics

scholarly journals Understanding Temporal Social Dynamics in Zoo Animal Management: An Elephant Case Study

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 882
Author(s):  
Ellen Williams ◽  
Samantha Bremner-Harrison ◽  
Carol Hall ◽  
Anne Carter
Keyword(s):  
Social Networks ◽  
Social Relationships ◽  
Social Dynamics ◽  
Temporal Dynamics ◽  
Interaction Networks ◽  
Positive Interaction ◽  
Animal Management ◽  
Management Procedures ◽  
Zoo Animals

Zoo animal management procedures which lead to changes to social groups can cause disruption in social hierarchies and the temporary breakdown of social relationships. Animals have different roles in social networks. Understanding individual positions in social networks is important for effective management and ensuring positive welfare for all animals. Using elephants as a case study, the aim of this research was to investigate temporal social dynamics in zoo animals. Behavioural data were collected between January 2016 and February 2017 from 10 African and 22 Asian elephants housed at seven zoos and safari parks in the UK and Ireland. Social interactions were defined as positive physical, positive non-physical, negative physical or negative non-physical. Social network analysis explored social relationships including the fluidity of networks over time and dyadic reciprocity. Social interaction networks were found to be fluid but did not follow a seasonal pattern. Positive interaction networks tended to include the entire social group whereas negative interactions were restricted to specific individuals. Unbalanced ties were observed within dyads, suggesting potential inequalities in relationships. This could impact on individual experiences and welfare. This research highlights subtle temporal dynamics in zoo elephants with the potential for species-level differences. Similar temporal dynamics may also be present in other socially housed zoo species. This research thus provides evidence for the importance of understanding the social networks of zoo animals over longer periods of time. Understanding social networks enables pro-active and evidence-based management approaches. Further research should seek to identify the minimum sampling efforts for social networks in a range of species, to enable the implementation of regular monitoring of social networks and thus improve the welfare of social species under human care.

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

scholarly journals Temporal properties of higher-order interactions in social networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giulia Cencetti ◽  
Federico Battiston ◽  
Bruno Lepri ◽  
Márton Karsai
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Social Dynamics ◽  
Temporal Dynamics ◽  
Higher Order ◽  
Order Structure ◽  
Face To Face ◽  
Work Settings ◽  
Temporal Properties ◽  
Social Settings

AbstractHuman social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people. Such interactions, approximating face-to-face communications, can be effectively represented as time varying social networks with links being unceasingly created and destroyed over time. Traditional analyses of temporal networks have addressed mostly pairwise interactions, where links describe dyadic connections among individuals. However, many network dynamics are hardly ascribable to pairwise settings but often comprise larger groups, which are better described by higher-order interactions. Here we investigate the higher-order organizations of temporal social networks by analyzing five publicly available datasets collected in different social settings. We find that higher-order interactions are ubiquitous and, similarly to their pairwise counterparts, characterized by heterogeneous dynamics, with bursty trains of rapidly recurring higher-order events separated by long periods of inactivity. We investigate the evolution and formation of groups by looking at the transition rates between different higher-order structures. We find that in more spontaneous social settings, group are characterized by slower formation and disaggregation, while in work settings these phenomena are more abrupt, possibly reflecting pre-organized social dynamics. Finally, we observe temporal reinforcement suggesting that the longer a group stays together the higher the probability that the same interaction pattern persist in the future. Our findings suggest the importance of considering the higher-order structure of social interactions when investigating human temporal dynamics.

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