Emergence and evolution of social networks through exploration of the Adjacent Possible space

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

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Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1713568115 ◽

2018 ◽

Vol 115 (7) ◽

pp. 1433-1438 ◽

Cited By ~ 49

Author(s):

Tim Gernat ◽

Vikyath D. Rao ◽

Martin Middendorf ◽

Harry Dankowicz ◽

Nigel Goldenfeld ◽

...

Keyword(s):

Social Networks ◽

Social Interactions ◽

Communication Networks ◽

Disease Transmission ◽

Large Scale ◽

Reference Network ◽

Interaction Patterns ◽

Human Communication ◽

Automated Monitoring ◽

Phone Calls

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

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Modelling the Diffusion of Investment Decisions on Modular Social Networks

Complexity ◽

10.1155/2020/2981231 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Xiaokang Cheng ◽

Narisa Zhao

Keyword(s):

Social Networks ◽

Community Structure ◽

Social Network ◽

Diffusion Model ◽

Financial Market ◽

Negative Impact ◽

Investment Decisions ◽

Negative Influence ◽

Clustering Coefficient ◽

The Impact

In the financial market, information and investment behaviors disseminate in investor social networks, and different contagion patterns may cause diverse investment trends. Prior studies have investigated the impact of investor social networks, but few have considered community structure. In this paper, we study the impact of the community structure of investor social networks on the diffusion of internet investment products. A two-stage diffusion model is proposed, and the clustering coefficient and modularity of an investor social network are considered. The results show that both modularity and the clustering coefficient have an impact on the diffusion velocity and scale and that the impact is most evident at the stage of explosive growth. The negative influence of a large modularity can be hardly mitigated by adjusting other factors. Furthermore, a decrease in modularity and an increase in the clustering coefficient can better facilitate diffusion when the temporary investment rate is high and can partly offset the negative impact of information discarding and divestment.

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Hostility and Perceptions of Support in Ambiguous Social Interactions

Journal of Individual Differences ◽

10.1027/1614-0001.27.2.108 ◽

2006 ◽

Vol 27 (2) ◽

pp. 108-115 ◽

Cited By ~ 5

Author(s):

Ana-Maria Vranceanu ◽

Linda C. Gallo ◽

Laura M. Bogart

Keyword(s):

Social Support ◽

Social Networks ◽

Information Processing ◽

Social Interactions ◽

Social Information Processing ◽

Perceived Social Support ◽

Social Information ◽

Inverse Association ◽

Processing Bias ◽

Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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Optimization Algorithms for Detection of Social Interactions

Algorithms ◽

10.3390/a13060139 ◽

2020 ◽

Vol 13 (6) ◽

pp. 139 ◽

Cited By ~ 1

Author(s):

Vincenzo Cutello ◽

Georgia Fargetta ◽

Mario Pavone ◽

Rocco A. Scollo

Keyword(s):

Social Networks ◽

Local Search ◽

Social Interactions ◽

Community Detection ◽

Experimental Analysis ◽

Search Strategy ◽

Random Search ◽

Optimization Methods ◽

Research Areas ◽

Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

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The switching mechanisms of social network densification

Scientific Reports ◽

10.1038/s41598-021-82432-9 ◽

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.

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An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

Big Data and Cognitive Computing ◽

10.3390/bdcc5010005 ◽

2021 ◽

Vol 5 (1) ◽

pp. 5

Author(s):

Ninghan Chen ◽

Zhiqiang Zhong ◽

Jun Pang

Keyword(s):

Machine Learning ◽

Social Networks ◽

Online Social Networks ◽

Exploratory Study ◽

Representation Learning ◽

Data Driven ◽

Learning Methods ◽

Twitter Users ◽

Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

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