Balance and fragmentation in societies with homophily and social balance

2021 ◽  
Author(s):  
TUAN PHAM ◽  
Andrew Alexander ◽  
Jan Korbel ◽  
Rudolf Hanel ◽  
Stefan Thurner
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Social Stress ◽  
Critical Value ◽  
Significant Fraction ◽  
Social Phenomena ◽  
Spin Models ◽  
Social Fragmentation ◽  
Social Balance

Abstract Recent attempts to understand the origin of social fragmentation on the basis of spin models include terms accounting for two social phenomena: homophily—the tendency for people with similar opinions to establish positive relations—and social balance—the tendency for people to establish balanced triadic relations. Spins represent attribute vectors that encode G different opinions of individuals; social interactions between individuals can be positive or negative. Here we present a co-evolutionary Hamiltonian framework that minimizes individuals’ social stress in social networks that have finite connectivity and people with a small number of attributes. We show that such systems always reach stationary, balanced, and fragmented states, if –in addition to homophily– individuals take into account a significant fraction, q, of their triadic relations. Above a critical value, qc, balanced and fragmented states exist for any number of opinions.

scholarly journals Balance and fragmentation in societies with homophily and social balance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan M. Pham ◽  
Andrew C. Alexander ◽  
Jan Korbel ◽  
Rudolf Hanel ◽  
Stefan Thurner
Keyword(s):  
Social Interactions ◽  
Critical Value ◽  
Significant Fraction ◽  
Social Phenomena ◽  
Spin Models ◽  
Social Fragmentation ◽  
Hamiltonian Model ◽  
Social Balance

AbstractRecent attempts to understand the origin of social fragmentation on the basis of spin models include terms accounting for two social phenomena: homophily—the tendency for people with similar opinions to establish positive relations—and social balance—the tendency for people to establish balanced triadic relations. Spins represent attribute vectors that encode G different opinions of individuals whose social interactions can be positive or negative. Here we present a co-evolutionary Hamiltonian model of societies where people minimise their individual social stresses. We show that societies always reach stationary, balanced, and fragmented states, if—in addition to homophily—individuals take into account a significant fraction, q, of their triadic relations. Above a critical value, $$q_c$$ q c , balanced and fragmented states exist for any number of opinions.

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 Topological measures for identifying and predicting the spread of complex contagions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas Guilbeault ◽  
Damon Centola
Keyword(s):  
Social Networks ◽  
Shortest Path ◽  
Empirical Data ◽  
Path Length ◽  
Centrality Measures ◽  
Social Phenomena ◽  
Node Centrality ◽  
Network Connectedness ◽  
Topological Measures ◽  
Definition Of

AbstractThe standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

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 Network Analysis of the Interaction between Different Religious and Philosophical Movements in Early Judaism

Philosophies ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 2
Author(s):  
Igor R. Tantlevskij ◽  
Ekaterina V. Gromova ◽  
Dmitry Gromov
Keyword(s):  
Social Networks ◽  
Network Analysis ◽  
Second Temple ◽  
Political Movements ◽  
Early Judaism ◽  
Social Balance ◽  
The Relationship

This paper presents an attempt to systematically describe and interpret the evolution of different religious and political movements in Judaea during the period of the Second Temple using the methods of the theory of social networks. We extensively analyzed the relationship between the main Jewish sects: Pharisees, Sadducees, Essenes (Qumranites), and later also Zealots. It is shown that the evolution of the relations between these sects agreed with the theory of social balance and their relations evolved toward more socially balanced structures.

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.

scholarly journals The ‘Selfish Element’: How Sperm and Egg Donors Construct Plausibly Moral Accounts of the Decision to Donate

Sociology ◽  
2021 ◽  
pp. 003803852110331
Author(s):  
Leah Gilman
Keyword(s):  
Social Interactions ◽  
Moral Self ◽  
Social Phenomena ◽  
Cultural Contexts ◽  
Sperm Donors ◽  
Selfish Element ◽  
Egg Donors ◽  
Sociological Studies ◽  
The Way

Multiple sociological studies have demonstrated how talk of ‘good’ motives enables people to maintain the presentation of a moral self in the context of stigmatised behaviours. Far fewer have examined why people sometimes describe acting for the ‘wrong reasons’ or choose to qualify, or reject, assumptions that they are motivated by a desire to ‘do good’. In this article, I analyse one such situation: sperm donors who describe being partially motivated by a ‘selfish’ desire to procreate, a motive which these same men frame as morally questionable. I argue that such accounts are explicable if we consider the (gendered) interactional and cultural contexts in which they are produced, particularly the way interactive contexts shape the desirability and achievability of plausibility and authenticity. I suggest that analysis of similar social phenomena can support sociologists in better understanding the complex ways in which moral practices are woven into social interactions.

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.

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