scholarly journals Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

2016 ◽  
Vol 113 (29) ◽  
pp. 8171-8176 ◽  
Author(s):  
Alin Coman ◽  
Ida Momennejad ◽  
Rae D. Drach ◽  
Andra Geana
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Large Scale ◽  
Optimal Strategies ◽  
Emergent Properties ◽  
Social Phenomena ◽  
Collective Memories ◽  
Individual Level ◽  
Social Network Structure ◽  
Social Interactionist

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

scholarly journals Offspring social network structure predicts fitness in families

2012 ◽  
Vol 279 (1749) ◽  
pp. 4914-4922 ◽  
Author(s):  
Nick J. Royle ◽  
Thomas W. Pike ◽  
Philipp Heeb ◽  
Heinz Richner ◽  
Mathias Kölliker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Conflicts Of Interest ◽  
Parus Major ◽  
Social Structures ◽  
Emergent Properties ◽  
Social Network Structure ◽  
The Social ◽  
The Individual

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

scholarly journals A Large-Scale Comparative Study of Informal Social Networks in Firms

2021 ◽  
Author(s):  
Abigail Z. Jacobs ◽  
Duncan J. Watts
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
Network Size ◽  
Emergent Properties ◽  
Network Data ◽  
Data Set ◽  
Network Characteristics ◽  
Individual Level ◽  
Industrial Sectors ◽  
Wide Range

Theories of organizations are sympathetic to long-standing ideas from network science that organizational networks should be regarded as multiscale and capable of displaying emergent properties. However, the historical difficulty of collecting individual-level network data for many (N ≫ 1) organizations, each of which comprises many (n ≫ 1) individuals, has hobbled efforts to develop specific, theoretically motivated hypotheses connecting micro- (i.e., individual-level) network structure with macro-organizational properties. In this paper we seek to stimulate such efforts with an exploratory analysis of a unique data set of aggregated, anonymized email data from an enterprise email system that includes 1.8 billion messages sent by 1.4 million users from 65 publicly traded U.S. firms spanning a wide range of sizes and 7 industrial sectors. We uncover wide heterogeneity among firms with respect to all measured network characteristics, and we find robust network and organizational variation as a result of size. Interestingly, we find no clear associations between organizational network structure and firm age, industry, or performance; however, we do find that centralization increases with geographical dispersion—a result that is not explained by network size. Although preliminary, these results raise new questions for organizational theory as well as new issues for collecting, processing, and interpreting digital network data. This paper was accepted by David Simchi-Levi, Special Issue of Management Science: 65th Anniversary.

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

scholarly journals The Network Structure of Police Misconduct

2019 ◽  
Vol 5 ◽  
pp. 237802311987979 ◽  
Author(s):  
George Wood ◽  
Daria Roithmayr ◽  
Andrew V. Papachristos
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Police Officers ◽  
Police Misconduct ◽  
Police Departments ◽  
Top Down ◽  
Network Ties ◽  
Individual Level ◽  
Factors Associated ◽  
Using Data

Conventional explanations of police misconduct generally adopt a microlevel focus on deviant officers or a macrolevel focus on the top-down organization of police departments. Between these levels are social networks of misconduct. This study recreates these networks using data on 16,503 complaints and 15,811 police officers over a six-year period in Chicago. We examine individual-level factors associated with receiving a complaint, the basic properties of these misconduct networks, and factors related to officer co-naming in complaints. We find that the incidence of police misconduct is associated with attributes including race, age, and tenure and that almost half of police officers are connected in misconduct ties in broader networks of misconduct. We also find that certain dyadic factors, especially seniority and race, strongly predict network ties and the incidence of group misconduct. Our results provide actionable information regarding possible ways to leverage the co-complaint network structure to reduce misconduct.

Cognition in a Social Context: A Social-Interactionist Approach to Emergent Phenomena

2018 ◽  
Vol 27 (5) ◽  
pp. 369-377 ◽  
Author(s):  
Madalina Vlasceanu ◽  
Karalyn Enz ◽  
Alin Coman
Keyword(s):  
Social Sciences ◽  
Social Context ◽  
Large Scale ◽  
Psychological Research ◽  
Fundamental Characteristic ◽  
Collective Memories ◽  
Emergent Phenomena ◽  
The Social ◽  
Social Interactionist ◽  
Interactionist Approach

The formation of collective memories, emotions, and beliefs is a fundamental characteristic of human communities. These emergent outcomes are thought to be the result of a dynamical system of communicative interactions among individuals. But despite recent psychological research on collective phenomena, no programmatic framework to explore the processes involved in their formation exists. Here, we propose a social-interactionist approach that bridges cognitive and social psychology to illuminate how microlevel cognitive phenomena give rise to large-scale social outcomes. It involves first establishing the boundary conditions of cognitive phenomena, then investigating how cognition is influenced by the social context in which it is manifested, and finally studying how dyadic-level influences propagate in social networks. This approach has the potential to (a) illuminate the large-scale consequences of well-established cognitive phenomena, (b) lead to interdisciplinary dialogues between psychology and the other social sciences, and (c) be more relevant for public policy than existing approaches.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

scholarly journals Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

2020 ◽  
Vol 11 (1) ◽  
pp. 18-24
Author(s):  
Morgan Prust ◽  
Abby Halm ◽  
Simona Nedelcu ◽  
Amber Nieves ◽  
Amar Dhand
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Network Structure ◽  
Network Size ◽  
Personal Network ◽  
Mapping Tool ◽  
Social Network Structure ◽  
Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

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.

scholarly journals Cumulative effects of triadic closure and homophily in social networks

2020 ◽  
Vol 6 (19) ◽  
pp. eaax7310 ◽  
Author(s):  
Aili Asikainen ◽  
Gerardo Iñiguez ◽  
Javier Ureña-Carrión ◽  
Kimmo Kaski ◽  
Mikko Kivelä
Keyword(s):  
Social Networks ◽  
Communication Networks ◽  
Empirical Studies ◽  
Network Evolution ◽  
Cumulative Effects ◽  
Small Individual ◽  
Individual Level ◽  
Social Network Structure ◽  
Triadic Closure ◽  
Dynamic Interplay

Social network structure has often been attributed to two network evolution mechanisms—triadic closure and choice homophily—which are commonly considered independently or with static models. However, empirical studies suggest that their dynamic interplay generates the observed homophily of real-world social networks. By combining these mechanisms in a dynamic model, we confirm the longheld hypothesis that choice homophily and triadic closure cause induced homophily. We estimate how much observed homophily in friendship and communication networks is amplified due to triadic closure. We find that cumulative effects of homophily amplification can also lead to the widely documented core-periphery structure of networks, and to memory of homophilic constraints (equivalent to hysteresis in physics). The model shows that even small individual bias may prompt network-level changes such as segregation or core group dominance. Our results highlight that individual-level mechanisms should not be analyzed separately without considering the dynamics of society as a whole.

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