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.

scholarly journals User Preference Dynamics on Evolving Social Networks - Learning, Modeling and Prediction

2019 ◽  
Author(s):  
Fabiola S. F. Pereira ◽  
Gina M. B. Oliveira ◽  
João Gama
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Temporal Dynamics ◽  
User Preferences ◽  
User Preference ◽  
Modeling And Prediction ◽  
Social Network Structure ◽  
Rich Information ◽  
Over Time

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks. Online social networks contain rich information about social interactions and relations, becoming essential source of knowledge for the understanding of user preferences evolution. In this thesis, we investigate the interplay between user preferences and social networks over time. We use temporal networks to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Our findings show that we can predict user preference changes by just observing how her social network structure evolves over time.

scholarly journals Modeling the Consequences of Social Distancing Over Epidemics Spreading in Complex Social Networks: From Link Removal Analysis to SARS-CoV-2 Prevention

2021 ◽  
Vol 9 ◽  
Author(s):  
M. Bellingeri ◽  
M. Turchetto ◽  
D. Bevacqua ◽  
F. Scotognella ◽  
R. Alfieri ◽  
...  
Keyword(s):  
Social Networks ◽  
Temporal Dynamics ◽  
Epidemiological Models ◽  
Connected Component ◽  
Epidemic Spreading ◽  
Social Distancing ◽  
Promising Tool ◽  
The Social ◽  
Network Topologies ◽  
Pharmaceutical Interventions

In this perspective, we describe how the link removal (LR) analysis in social complex networks may be a promising tool to model non-pharmaceutical interventions (NPIs) and social distancing to prevent epidemics spreading. First, we show how the extent of the epidemic spreading and NPIs effectiveness over complex social networks may be evaluated with a static indicator, that is, the classic largest connected component (LCC). Then we explain how coupling the LR analysis and type SIR epidemiological models (EM) provide further information by including the temporal dynamics of the epidemic spreading. This is a promising approach to investigate important aspects of the recent NPIs applied by government to contain SARS-CoV-2, such as modeling the effect of the social distancing severity and timing over different network topologies. Further, implementing different link removal strategies to halt epidemics spreading provides information to individuate more effective NPIs, representing an important tool to offer a rationale sustaining policies to prevent SARS-CoV-2 and similar epidemics.

scholarly journals Using multilayer network analysis to explore the temporal dynamics of collective behavior

2020 ◽  
Author(s):  
David N Fisher ◽  
Noa Pinter-Wollman
Keyword(s):  
Social Networks ◽  
Network Analysis ◽  
Social Interactions ◽  
Network Structure ◽  
Collective Behavior ◽  
Temporal Dynamics ◽  
Multilayer Network ◽  
Collective Behaviors ◽  
The Social ◽  
Over Time

Abstract Social organisms often show collective behaviors such as group foraging or movement. Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals. When social interactions change over time, collective behaviors may change because these behaviors emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behavior is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modeled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.

scholarly journals Using the relational event model (REM) to investigate the temporal dynamics of animal social networks

2015 ◽  
Vol 101 ◽  
pp. 99-105 ◽  
Author(s):  
Mark Tranmer ◽  
Christopher Steven Marcum ◽  
F. Blake Morton ◽  
Darren P. Croft ◽  
Selvino R. de Kort
Keyword(s):  
Social Networks ◽  
Temporal Dynamics ◽  
Event Model ◽  
Animal Social Networks

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
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