scholarly journals Fundamental structures of dynamic social networks

2016 ◽  
Vol 113 (36) ◽  
pp. 9977-9982 ◽  
Author(s):  
Vedran Sekara ◽  
Arkadiusz Stopczynski ◽  
Sune Lehmann
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Communication Networks ◽  
Social Dynamics ◽  
Demographic Data ◽  
Social Systems ◽  
Telecommunication Networks ◽  
High Resolution Data ◽  
Online Social Media ◽  
Dynamic Social Networks

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

Ideology, communication and polarization

2021 ◽  
Vol 376 (1822) ◽  
pp. 20200133
Author(s):  
Yoshihisa Kashima ◽  
Andrew Perfors ◽  
Vanessa Ferdinand ◽  
Elle Pattenden
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Political Discourse ◽  
Network Dynamics ◽  
Political Polarization ◽  
Connectionist Model ◽  
Theme Issue ◽  
Social Network Dynamics ◽  
Dynamic Social Networks ◽  
Echo Chamber

Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular, we explicitly model ideologically filtered interpretation of social information, ideological commitment to initial opinion, and communication on dynamically evolving social networks, and examine how these factors combine to generate ideologically divergent and polarized political discourse. The results show that ideological interpretation and commitment tend towards polarized discourse. Nonetheless, communication and social network dynamics accelerate and amplify polarization. Furthermore, when agents sever social ties with those that disagree with them (i.e. structure their social networks by homophily), even non-ideological agents may form an echo chamber and form a cluster of opinions that resemble an ideological group. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.

scholarly journals Reciprocity and behavioral heterogeneity govern the stability of social networks

2020 ◽  
Vol 117 (6) ◽  
pp. 2993-2999 ◽  
Author(s):  
Roslyn Dakin ◽  
T. Brandt Ryder
Keyword(s):  
Social Networks ◽  
Social Connectedness ◽  
Social Dynamics ◽  
Social Systems ◽  
Bird Species ◽  
Network Stability ◽  
Apparent Paradox ◽  
Trade Off ◽  
Behavioral Heterogeneity ◽  
The Stability

The dynamics of social networks can determine the transmission of information, the spread of diseases, and the evolution of behavior. Despite this broad importance, a general framework for predicting social network stability has not been proposed. Here we present longitudinal data on the social dynamics of a cooperative bird species, the wire-tailed manakin, to evaluate the potential causes of temporal network stability. We find that when partners interact less frequently and when social connectedness increases, the network is subsequently less stable. Social connectivity was also negatively associated with the temporal persistence of coalition partnerships on an annual timescale. This negative association between connectivity and stability was surprising, especially given that individual manakins who were more connected also had more stable partnerships. This apparent paradox arises from a within-individual behavioral trade-off between partnership quantity and quality. Crucially, this trade-off is easily masked by behavioral variation among individuals. Using a simulation, we show that these results are explained by a simple model that combines among-individual behavioral heterogeneity and reciprocity within the network. As social networks become more connected, individuals face a trade-off between partnership quantity and maintenance. This model also demonstrates how among-individual behavioral heterogeneity, a ubiquitous feature of natural societies, can improve social stability. Together, these findings provide unifying principles that are expected to govern diverse social systems.

Using Quantum Agent-Based Simulation to Model Social Networks

2011 ◽  
pp. 42-54
Author(s):  
C. Bisconti ◽  
A. Corallo ◽  
M. De Maggio ◽  
F. Grippa ◽  
S. Totaro
Keyword(s):  
Social Networks ◽  
Quantum Mechanics ◽  
Quantum Physics ◽  
Business Processes ◽  
Social Dynamics ◽  
Social Systems ◽  
Many Body ◽  
Agent Based ◽  
Microscopic Interactions ◽  
The Many

This research aims to apply models extracted from the many-body quantum mechanics to describe social dynamics. It is intended to draw macroscopic characteristics of organizational communities starting from the analysis of microscopic interactions with respect to the node model. In this chapter, the authors intend to give an answer to the following question: which models of the quantum physics are suitable to represent the behaviour and the evolution of business processes? The innovative aspects of the project are related to the application of models and methods of the quantum mechanics to social systems. In order to validate the proposed mathematical model, the authors intend to define an open-source platform able to model nodes and interactions within a network, to visualize the macroscopic results through a digital representation of the social networks.

Large-scale dynamic social data representation for structure feature learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5253-5262
Author(s):  
Xiaoxian Zhang ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Dynamic Change ◽  
Feature Learning ◽  
Data Representation ◽  
Feature Representation ◽  
Small Scale ◽  
Dynamic Feature ◽  
Dynamic Social Networks

The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

scholarly journals Clumpiness: Modeling the Impact of Social Dynamics on COVID-19 Spread

2021 ◽  
Author(s):  
Ben Goertzel ◽  
Cassio Pennachin ◽  
Deborah Duong ◽  
Matthew Iklé ◽  
Michael Duncan ◽  
...  
Keyword(s):  
Social Networks ◽  
Disease Transmission ◽  
Social Dynamics ◽  
Demographic Data ◽  
Herd Immunity ◽  
Control Policy ◽  
Policy Formation ◽  
Packet Transmission ◽  
Physical Proximity ◽  
The Impact

We present an agent based simulation supplemented with two novel social network interconnectivity measures, `clumpiness' and `hoprank,' that are the same concept defined at global and local levels, respectively. The measures may be computed from samples of readily available demographic data, and are useful for measuring probabilistic packet transmission through social networks. For simplicity, agents in our simulation group together through homophily, the principle of `like attracts like'. In three studies we apply clumpiness to measure the effects, on disease transmission, caused by social networks of both homophilic physical proximity and homophilic information replication. The particular characteristic we are interested in about disease transmission is herd immunity, the percentage of a population that has to be immune in order to prevent infection from spreading to those who are not. Two studies demonstrate innovations measuring herd immunity levels and predicting future outbreak locations, procedures relevant to epidemiological control policy. In the first study, we look at how homophilic physical proximity networks form natural bubbles that act as frictive surfaces that affect the speed of transmission of packets and influence herd immunity levels. In the second study, we test clumpiness in homophilic proximity social networks as a predictor of future infection outbreaks at the level of individual schools, restaurants, and workplaces. Our third study demonstrates that protective social bubbles form naturally from homophilic information replication networks, and enhance the natural bubbles that come from the homophilic physical proximity networks. Accurate description of this information environment lays the foundation for epidemiological messaging policy formation.

scholarly journals Identifying Influencers in Social Networks

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 450 ◽  
Author(s):  
Xinyu Huang ◽  
Dongming Chen ◽  
Dongqi Wang ◽  
Tao Ren
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Daily Life ◽  
Multidisciplinary Research ◽  
Multilayer Network ◽  
Online Social Media ◽  
Huge Data ◽  
Real World Datasets ◽  
Shed Light

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

Crowd attraction-driven community evolution on social network

2021 ◽  
pp. 2250009
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Large Scale ◽  
Detection Algorithm ◽  
Information Propagation ◽  
Evolution Analysis ◽  
Dynamic Social Networks ◽  
Community Evolution ◽  
Community Detection Algorithm

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

Working Adults' Buying Intention Through Online Social Network

2021 ◽  
Vol 17 (4) ◽  
pp. 92-116
Author(s):  
Syed Shah Alam ◽  
Chieh-Yu Lin ◽  
Mohd Helmi Ali ◽  
Nor Asiah Omar ◽  
Mohammad Masukujjaman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Perceived Risk ◽  
Word Of Mouth ◽  
Online Social Network ◽  
Perceived Value ◽  
Online Social Media ◽  
Starting Point

Most businesses have online social media presence; therefore, understanding of working adult's perception on buying through online social networks is vital. The aim of this study is to examine the effect of perceived value, sociability, usability, perceived risk, trust, and e-word-of-mouth on buying intention through online social network sites. The research model for this study was developed based on the literature on information system research. This study adopted convenient sampling of non-probability sampling procedure. Data were collected through self-administered questionnaire, and PLS-based path analysis was used to analyse responses. The findings of the study shows that perceived value, sociability, usability, e-word-of-mouth, attitude, and subjective norm are significant constructs of buying intention through online social networks. This research can serve as a starting point for online shopping research through online social media while encouraging further exploration and integration addition adoption constructs.

A Study Regarding Human Perception of a Company through Social Media

2019 ◽  
Vol 34 ◽  
pp. 309-314
Author(s):  
Mirona Ana Maria Popescu ◽  
Olivia Doina Negoiță ◽  
Anca Purcărea ◽  
Markus Helfert
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Human Perception ◽  
Social Media Networks ◽  
Online Social Media ◽  
The Social ◽  
Key Indicators ◽  
Different Characteristics ◽  
A Company

Of the utmost importance is finding the social networks that best fit to an industry, a company, its products / services, and last but not least, with the target audience. Each social network has different characteristics and, in addition, a different philosophy.The authors aim to carry out a bibliographic research in this paper to highlight the extent to which social networks are used. As a result, a top of social networks will be built to help raise awareness, promote products, and consolidate a strong customer-company relationship. The authors will also realize a statistical analysis of online social media networks to determine their key indicators, traffic on each platform, time spent by a user on that platform, and other key indicators, through an online tool. The results of this paper consist in presenting two classifications: the first from the perspective of the companies and the second from the perspective of social network users.

scholarly journals Are forum networks social networks? A methodological perspective

2019 ◽  
Author(s):  
Sasha Poquet ◽  
Liubov Tupikina ◽  
Marc Santolini
Keyword(s):  
Social Network ◽  
Communication Networks ◽  
Network Structure ◽  
Social Dynamics ◽  
Network Models ◽  
Clustering Coefficient ◽  
Edge Weight ◽  
Digital Learning ◽  
Online Forums ◽  
Local Clustering Coefficient

The mission of learning analytics (LA) is to improve learner experiences using the insights from digitally collected learner data. While some areas of LA are maturing, this is not consistent across all LA specialisations. For instance, LA for social learning lack validated approaches to account for the effects of cross-course variability in learner behavior. Although the associations between network structure and learning outcomes have been examined in the context of online forums, it remains unclear whether such associations represent bona fide social effects, or merely reflect heterogeneity in individual posting behavior, leading to seemingly complex but artefactual social network structures. We argue that to start addressing this issue, posting activity should be explicitly included and modelled in forum network representations. To gain insight to what extent learner degree and edge weight are merely derivatives of learner activity, we construct random models that control for the level of posting and post properties, such as popularity and thread hierarchy level. Analysis of forum networks in twenty online courses presented in this paper demonstrates that individual posting behavior is highly predictive of both the breadth (degree) and frequency (strength) in forum communication networks. This implies that, in the context of forum-based modelling, degree and frequency may not reflect the social dynamics. However, results suggest that clustering of the network structure is not a derivative of individual posting behaviour. Hence, weighted local clustering coefficient may be a better proxy for social relationships. The empirical results are relevant to scientists interested in social interactions and learner networks in digital learning, and more generally to researchers interested in deriving informative social network models from online forums.

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