scholarly journals Infertile Japanese women's perception of positive and negative social interactions within their social networks

2008 ◽  
Vol 23 (12) ◽  
pp. 2737-2743 ◽  
Author(s):  
Y. Akizuki ◽  
I. Kai
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Negative Social Interactions

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
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.

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
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.

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

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.

Deficits in Eye Gaze During Negative Social Interactions in Patients With Schizophrenia

2010 ◽  
Vol 198 (11) ◽  
pp. 829-835 ◽  
Author(s):  
Soo-Hee Choi ◽  
Jeonghun Ku ◽  
Kiwan Han ◽  
Eosu Kim ◽  
Sun I. Kim ◽  
...  
Keyword(s):  
Social Interactions ◽  
Eye Gaze ◽  
Negative Social Interactions

Should Strengthening Bonds Be a Public Health Priority?

2021 ◽  
Vol 28 (2) ◽  
pp. 79-87
Author(s):  
Valerie M. Wood ◽  
Heather Stuart
Keyword(s):  
Physical Activity ◽  
Health Behaviors ◽  
Physical Health ◽  
Social Interactions ◽  
Social Relationships ◽  
Health Conditions ◽  
Emotional Bonds ◽  
Important Health ◽  
Negative Social Interactions

Abstract. Background: Previous research demonstrates the importance of close relationships on our physical health. However, to what extent the quality of our social relationships impacts our health, relative to other important health behaviors (e.g., smoking, drinking alcohol, and physical exercise), is less clear. Aims: Our goal was to use a nationally representative sample of Canadian adults to assess the relative importance of the quality of one’s social relationships (close emotional bonds and negative social interactions), relative to important health behaviors on physical health outcomes previously linked to social relationship quality. Method: Data ( N = 25,113) came from the Canadian Community Health Survey in 2012, a cross-sectional survey administered by Statistics Canada (2013) . The predictor variables were the presence of close emotional bonds, negative social relationships, type of smoker, type of drinker, and weekly hours of physical activity. The outcome variables were a current or previous diagnosis of high blood pressure, cancer, stroke, reports of current illness or injury, pain, and self-reported physical health. Results: Using regressions, we found that negative social interactions were more important than other health behaviors in relation to current injury/illness and pain. Physical activity was most strongly related to self-rated health, followed by negative social interactions and then close emotional bonds. Alcohol consumption was more related to the prevalence of stroke. Conclusions: Our findings suggest that negative social interactions may be more related to acute or minor physical health conditions, but social relationships may not be more strongly related to more chronic, life-threatening health conditions than other health behaviors.

scholarly journals Social networks and unemployment exit in Great Britain

2018 ◽  
Vol 45 (8) ◽  
pp. 1205-1226
Author(s):  
Panos Sousounis ◽  
Gauthier Lanot
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Empirical Studies ◽  
Unemployment Duration ◽  
Empirical Literature ◽  
Job Seekers ◽  
Content Type ◽  
Job Attainment ◽  
Discrete Time Hazard Model ◽  
Direct Measures

Purpose The purpose of this paper is to examine the effect employed friends have on the probability of exiting unemployment of an unemployed worker according to his/her educational (skill) level. Design/methodology/approach In common with studies on unemployment duration, this paper uses a discrete-time hazard model. Findings The paper finds that the conditional probability of finding work is between 24 and 34 per cent higher per period for each additional employed friend for job seekers with intermediate skills. Social implications These results are of interest since they suggest that the reach of national employment agencies could extend beyond individuals in direct contact with first-line employment support bureaus. Originality/value Because of the lack of appropriate longitudinal information, the majority of empirical studies in the area assess the influence of social networks on employment status using proxy measures of social interactions. The current study contributes to the very limited empirical literature of the influence of social networks on job attainment using direct measures of social structures.

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

2018 ◽  
Vol 115 (7) ◽  
pp. 1433-1438 ◽  
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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