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 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.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

scholarly journals Ego-Centric Social Network Analysis in Marketing of Fish From Cage Culture System In Simalungun District, Indonesia

2019 ◽  
Vol 8 (4) ◽  
pp. 105-113
Author(s):  
Feriel Amelia Sembiring ◽  
Fikarwin Zuska ◽  
Bengkel Ginting ◽  
Rizabuana Ismail ◽  
Henry Sitorus
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Culture System ◽  
Qualitative Approach ◽  
Mutual Trust ◽  
Cage Culture ◽  
The Social ◽  
The Village ◽  
The Relationship

Aquaculture of Cage Culture is one of the main activities carried out by the community in the village of Haranggaol to fulfill their economic needs. This cultivation business establishes a relationship between traders and cages in terms of marketing their crops. There are 3 egocentric actors in the Haranggaol area. They are collectors (entrepreneurs/farmers who own capital), namely the Rohakinian group, the Siharo group, and the Paimaham group. Through these three egocentric actors, a social network is formed with several alters. Based on the qualitative approach with use Ucinet software, the mapping of their social networks can be seen as follows: alter actors connected to the Rohakinian group are 12 farmers in the group and 2 farmers outside the group with a density of 0.033. There are 27 alter actors connected to the Siharo group, 21 from the group and 6 from outside the group with a density of 0.014. There are 27 alter actors connected to the Paimaham group, namely 36 farmers from their groups and 10 farmers outside the group with a density of 0.005. The social networks that occur between these actors are intertwined due to the existence of kinship relationships, family or close friends who know each other among them. The relationship between family, family or close friends built with mutual trust make this network integrated.

scholarly journals Analysis of User Social Support Network in Online Tumor Community

2020 ◽  
Vol 5 (1) ◽  
pp. 184-194
Author(s):  
Hui Lin ◽  
Shijuan Li
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Network Analysis ◽  
Personal Information ◽  
Internet Technology ◽  
Support Network ◽  
Social Support Network ◽  
Information Behaviors ◽  
The Social ◽  
Professional Health

AbstractWith the development of Internet technology, online health forums have become indispensable for people who seek non-professional health support. This research focuses on the content posted by cancer patients and their relatives in online health forums and social networks to raise the following research questions: What is the overall view of the social support network in the online tumor community? What are the information behaviors of the online tumor community in different identities of users? How users interact in this community and build this network of social support? What are the topics users would like to share and talk about? What kinds of users could be the key users in this community? Method: Using the post and comment data of the Oncology Forum of Tianya Hospital in 2019, combined with social network analysis and word co-occurrence network analysis, the following conclusions are obtained: (1) There are some central points in the overall social support network, and there are central users consistent with other social networks. (2) Positive users are more likely to comment on others, and it is easier to get others’ comments, while negative users are more likely to share personal information and do not want to participate more in social interaction. (3) Users focus on posting emotional and emotional content in content sharing. Information-based social support information. The social support experience that this type of information brings to users can be positive and negative. (4) The most active group in the patients’ online health community, followed by the patients’ children. (5) The relationship between users and patients is diverse and there are two types of singularity. Users with diverse relationships are more likely to be commented on, and they are more willing to comment on users who also have diverse relationships.

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

Entanglement, Materiality and the Social Organisation of Construction Workers in Classical Athens

2018 ◽  
pp. 512-528
Author(s):  
Diane Harris Cline
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Theory ◽  
Actor Network Theory ◽  
Fifth Century ◽  
Actor Network ◽  
The Social ◽  
Building Program

This chapter views the “Periclean Building Program” through the lens of Actor Network Theory, in order to explore the ways in which the construction of these buildings transformed Athenian society and politics in the fifth century BC. It begins by applying some Actor Network Theory concepts to the process that was involved in getting approval for the building program as described by Thucydides and Plutarch in his Life of Pericles. Actor Network Theory blends entanglement (human-material thing interdependence) with network thinking, so it allows us to reframe our views to include social networks when we think about the political debate and social tensions in Athens that arose from Pericles’s proposal to construct the Parthenon and Propylaea on the Athenian Acropolis, the Telesterion at Eleusis, the Odeon at the base of the South slope of the Acropolis, and the long wall to Peiraeus. Social Network Analysis can model the social networks, and the clusters within them, that existed in mid-fifth century Athens. By using Social Network Analysis we can then show how the construction work itself transformed a fractious city into a harmonious one through sustained, collective efforts that engaged large numbers of lower class citizens, all responding to each other’s needs in a chaine operatoire..

Using Network Analysis for Understanding How Decisions are Made

2011 ◽  
pp. 950-957
Author(s):  
Frédéric Adam
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Decision Aids ◽  
Network Approach ◽  
Organizational Decision Making ◽  
Traditional Research ◽  
The Social ◽  
High Level

Network analysis, a body of research that concentrates on the social networks that connect actors in society, has been found to have many applications in areas where researchers struggle to understand the complex workings of organisations (Nohria, 1992). Social network analysis (SNA) acknowledges that individuals are characterised just as much by their relationships with one another (which is often neglected in traditional research) as by their specific attributes (Knoke & Kuklinski, 1982) and that, beyond individuals, society itself is made of networks (Kilduff & Tsai, 2003). It is the study of the relationships between actors and between clusters of actors in organisations and in society that has been labeled network analysis. These high level observations about network analysis indicate that this orientation has great potential for the study of how managers, groups of managers, and organisations make decisions, following processes that unfold over long periods of time and that are sometimes very hard to fully comprehend without reference to a network approach. This article proposes to investigate the potential application of network analysis to the study of individual and organizational decision making and to leverage its strengths for the design and development of better decision aids.

scholarly journals Cultural selection shapes network structure

2019 ◽  
Vol 5 (8) ◽  
pp. eaaw0609 ◽  
Author(s):  
Marco Smolla ◽  
Erol Akçay
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Cultural Evolution ◽  
Level Structure ◽  
Population Level ◽  
Trade Off ◽  
Skill Sets ◽  
Individual Level ◽  
Dense Networks ◽  
The Social

Cultural evolution relies on the social transmission of cultural traits along a population’s social network. Research indicates that network structure affects information spread and thus the capacity for cumulative culture. However, how network structure itself is driven by population-culture co-evolution remains largely unclear. We use a simple model to investigate how populations negotiate the trade-off between acquiring new skills and getting better at existing skills and how this trade-off shapes social networks. We find unexpected eco-evolutionary feedbacks from culture onto social networks and vice versa. We show that selecting for skill generalists results in sparse networks with diverse skill sets, whereas selecting for skill specialists results in dense networks and a population that specializes on the same few skills on which everyone is an expert. Our model advances our understanding of the complex feedbacks in cultural evolution and demonstrates how individual-level behavior can lead to the emergence of population-level structure.

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