Changes In Social Networks Research Articles

The Spread of Ideas in a Network-The Garbage-Can Model.

The main goal of our work is to show how ideas change in social networks. Our analysis is based on three concepts: (i) temporal networks, (ii) the Axelrod model of culture dissemination, (iii) the garbage can model of organizational choice. The use of the concept of temporal networks allows us to show the dynamics of ideas spreading processes in networks, thanks to the analysis of contacts between agents in networks. The Axelrod culture dissemination model allows us to use the importance of cooperative behavior for the dynamics of ideas disseminated in networks. In the third model decisions on solutions of problems are made as an outcome of sequences of pseudorandom numbers. The origin of this model is the Herbert Simon’s view on bounded rationality. In the Axelrod model, ideas are conveyed by strings of symbols. The outcome of the model should be the diversity of evolving ideas as dependent on the chain length, on the number of possible values of symbols and on the threshold value of Hamming distance which enables the combination.

The Role of Social Adjustment in a Collegiate Behavioral Activation Program.

The transition to college is associated with significant changes in social support networks and concomitant increases in depressive symptoms. First-year students who are more socially engaged within their new academic settings may experience greater overall wellbeing. Behavioral activation (BA) is an evidence-based intervention which promotes individuals' engagement with valued activities and has been examined as a possible primary prevention for depressive symptoms among first-year students. Yet, the important role of social adjustment, and its impact on students' activity level, has not yet been considered. The current study is a secondary data analysis of research evaluating a BA-based intervention embedded into a first-year orientation course. The aim of the project was to evaluate the efficacy of BA on improving social adjustment and the effect of social adjustment on subsequent depressive symptoms. A diverse sample of college students (n = 71) attending a state university in the mid-Atlantic region reported on their levels of depression, behavioral activation, and social adjustment. Students then received either BA or standard programming. Results suggest that improved engagement in valued activities at mid-intervention was associated with increases in students' perceptions of their own social adjustment. This, in turn, predicted steeper decreases in rates of depressive symptoms post-intervention. Findings also indicate that greater social adjustment improved the efficacy of a BA-based intervention in reducing depressive symptoms, but had no impact on depressive symptoms for students receiving the standard orientation programming.

Social brain network predicts real-world social network in individuals with social anhedonia

Social anhedonia (SA) impairs social functioning in schizophrenia. Previous evidence suggested that certain brain regions predict longitudinal change of real-world social outcomes, yet previous study designs have failed to capture the corresponding functional connectivity among the brain regions involved. This study measured the real-world social network in 22 pairs of individuals with high and low levels of SA, and followed up them for 21 months. We further explored whether resting-state social brain network characteristics could predict the longitudinal variations of real-world social network. Our results showed that social brain network characteristics could predict the change of real-world social networks in both the high SA and low SA groups. However, the results differed between the two groups, i.e., the topological characteristics of the social brain network predicted real-world social network change in the high SA group; whereas the functional connectivity within the social brain network predicted real-world social network change in the low SA group. Principal component analysis and linear regression analysis on the entire sample showed that the functional connectivity component centered at the right orbital inferior frontal gyrus could best predict social network change. Our findings support the notion that social brain network characteristics could predict social network development.

The impact of social network change and health decline: a qualitative study on experiences of older adults who are ageing in place

BackgroundOlder adults prefer to age in place. Social network change and health decline challenge ageing in place, as stressors that make age-related advantages disappear. The aim of this study was to explore social network change and health decline and its impact on older adults who are ageing in place.MethodIn-depth interviews (n = 16) were conducted with older adults who were ageing in place and who were experiencing health decline and social network change. Procedures for grounded theory building were followed to analyse the interviews with respondents who were discharged from the hospital less than 4 months ago (n = 7). Narrative analysis was conducted to reach a deeper understanding of the expected complexity of experiences of this targeted sample.ResultsResults encompass a typology with four types of impact: A. Sneak preview of old age, B. Disruptive transition into old age, C. Drastically ageing, and D. Steadily ageing. Additionally, indications were found that older adults should be able to move along the four types of impact and ideally could end up in quartile D, experiencing little or no impact at all (anymore).ConclusionThe results present an optimistic view on the possibilities of older adults to continue ageing in place despite experiencing unavoidable and uncontrollable stressors in life. Also, the results provide leads for practice, to develop an action perspective for home care nurses and gerontological social workers to determine and reduce the impact of social network change and health decline on older adults who are ageing in place. Suggestions for further research would be to unravel how to detect temporal setbacks in successful ageing in place.

MODEC — Modeling and Detecting Evolutions of Communities

Social network analysis encompasses the study of networked data and examines questions related to structures and patterns that can lead to the understanding of the data and the intrinsic relationships, such as identifying influential nodes, recognizing critical paths, predicting unobserved relationships, discovering communities, etc. All of these analyses, germane to a variety of application domains, are typically done on static information networks; that is, a fixed snapshot of the information network. Yet, a social network changes and understanding the evolution of the network and detecting these changes in the underlying structures is paramount for a multitude of applications. Looking at networks as fixed snapshots misses the opportunity to capture the evolutionary patterns. In this paper, we present a framework for modeling community evolution in social networks by tracking of events related to the life cycle of a community. We illustrate the capabilities of our framework by applying it to real datasets and validate the results using topics extracted from the tracked communities.

The Effects of Promotion on Tie Formation and Decay across Team and Rank Boundaries

This study investigates how promotion affects tie formation and decay across team and rank boundaries in an organization. It also investigates the moderating roles of Machiavellian personality in the relationship between promotion and inter-team tie formation and decay. Online survey data were collected in two time points from 566 employees of a Korean chemical company. The results from negative binomial regressions generally confirm our hypotheses that promotion would be significantly related to inter-team tie formation and decay of newly promoted employees with both higher-and lower-rank employees, but not to intra-team social network changes. The results also confirm the positive and significant moderating effect of Machiavellian personality on the relationship between promotion and inter-team tie formation with higher-rank employees. These results are robust to different specification tests. Overall, this study suggests that newly promoted employees tend to conserve the total amount of social networks, by forming new social ties and decaying existing ties at the same time, and they do so partly with instrumental motivations.

Volunteering and health: The role of social network change

ObjectivesLevels of volunteering may differentially influence multiple dimensions of health among older adults. Further, increasing evidence indicates social networks represent critical bridging and bonding contexts for the volunteering-health link. This study examines two research questions: 1) does volunteering level (low, moderate, high) influence physical and mental health in the same ways? And 2) does social network change moderate this link? MethodsData come from Waves 1 and 2 of the longitudinal Social Relations Study (n = 556) collected in 1992 and 2005 and the sub-sample of adults age 50 and older at Wave 2. Regression analyses predicting self-rated health and depressive symptoms were conducted to examine main effects of volunteering and moderating effects of social network change. ResultsVolunteering at a moderate level (101–300 hours per year) was associated with fewer depressive symptoms compared to those not volunteering. Social network change moderated the association between volunteering and self-rated health. Among those reporting a decrease in the proportion of non-family in their network (decrease in bridging), a moderate level of volunteering was associated with better self-rated health. ConclusionWhile moderate levels of volunteering are associated with better mental health, the link to physical health is only present in the context of decreasing network bridging. Identifying specific circumstances under which volunteering is beneficial is critical for developing interventions to promote health for all, including those in mid and later life.

Social network changes during the development of immature capuchin monkeys (Sapajus spp.).

Immatureindivuduals influence the formation and maintenance of social relationships within groups in diverse ways. Because of the increased interest of group members toward newborns, lactating females may use infants as social tools to temporally gain rank positions in matrilineal societies, and differential support received by the mothers may bias the network of immatures born to females of different ranks. In this study, we investigated the changes in proximity, grooming, play, and agonism networks of lactating females and immatures of different developmental periods, sex, and mothers' dominance rank. A semi-free-ranging group of 22 capuchin monkeys (Sapajus spp) was monitored for 12months, totaling over 300 hours of observation. During this period, the age changes of 13 immatures were monitored and recorded. Best regression models showed that an increased number of grooming partners while lactating did not translate into changes in the proximity or agonistic network positions for females. Age was the main predictor of social network changes, while sex had a minor influence on the play network and no influence on the grooming or agonistic networks. Finally, mothers' rank predicted differences in the affiliative but not the agonistic social network. This pattern points to a more affiliative and individual-based rather than agonistic and nepotism-based strategy for social network insertion, which can be explained by the decreased competition faced by the focal group and by the behavioral flexibility of the clade.

The Role of Social Isolation and the Development of Depression. A Comparison of the Widowed and Married Oldest Old in Germany.

Widowhood is common in old age, can be accompanied by serious health consequences and is often linked to substantial changes in social network. Little is known about the impact of social isolation on the development of depressive symptoms over time taking widowhood into account. We provide results from the follow-up 5 to follow-up 9 from the longitudinal study AgeCoDe and its follow-up study AgeQualiDe. Depression was measured with GDS-15 and social isolation was assessed using the Lubben Social Network Scale (LSNS-6). The group was aligned of married and widowed people in old age and education through entropy balancing. Linear mixed models were used to examine the frequency of occurrence of depressive symptoms for widowed and married elderly people depending on the risk of social isolation. Our study shows that widowhood alone does not lead to an increased occurrence of depressive symptoms. However, “widowed oldest old”, who are also at risk of social isolation, have significantly more depressive symptoms than those without risk. In the group of “married oldest old”, women have significantly more depressive symptoms than men, but isolated and non-isolated do not differ. Especially for people who have lost a spouse, the social network changes significantly and increases the risk for social isolation. This represents a risk factor for the occurrence of depressive symptoms.

AFIF: Automatically Finding Important Features in community evolution prediction for dynamic social networks

Applications of Intelligent systems have increased the impact of modeling and analyzing in dynamic social networks. For effective decision making, models must be able to forecast the outcome of each option and determine which option is the best for a particular situation. In this context, community evolution prediction is a challenging and time-consuming task, which is the extraction of structural features from large real-world networks. We present AFIF, Automatically Finding Important Features, an efficient solution to examine communities’ structural features and also to find a proper subset of promising features in order to predict the upcoming changes of social networks. AFIF combines two key concepts to find prominent features: (i) Prioritization of attributes based on their Spearman’s correlation with other features. This enables us to know the features that can represent the rest and to explore which features are unique compared to others. (ii) Training a boosting learner and prioritizing attributes based on their usage frequency in learning process to realize which features are more valuable. Eventually, important features are determined by random forest classifier. We have then conducted extensive experiments and confirmed that our selection of features delivers an outstanding performance in contrast with using the entire set of features.

The Association Between Cannabis Use and Perceived Social Support: the Mediating Role of Decreased Social Network

In recent years, accumulating evidence has pointed to the possible negative effects of cannabis use in social and interpersonal context. In this study, we explored the association between level of cannabis use and perceived social support and the extent to which social network size and diversity may mediate this association. Data from the NESARC-III was analyzed. Participants were either non-cannabis users (N = 32,523), using cannabis without qualifying for a diagnosis of cannabis use disorder (N = 2729), or qualifying for Cannabis Use Disorder (CUD; N = 972). Perceived social support was measured by the Interpersonal Support Evaluation List-12 and social network size and diversity were measured using the “Social Network Index.” Baron and Kenny’s statistical method was used for mediation analyses, with control for possible covariates and Bonferroni corrections applied. After controlling for covariates, social network size and diversity independently and fully mediated the association between level of cannabis use and general perceived social support (direct effect: b = −0.02, p = 0.0521, and b = −0.02, p = 0.057, respectively). According to these analyses, level of cannabis use was significantly associated with decreased social network size and diversity, which in turn were associated with lower perceived social support. Our findings highlight the need to address possible changes in social network among cannabis users.

Big Events theory and measures may help explain emerging long-term effects of current crises

ABSTRACT Big Events are periods during which abnormal large-scale events like war, economic collapse, revolts, or pandemics disrupt daily life and expectations about the future. They can lead to rapid change in health-related norms, beliefs, social networks and behavioural practices. The world is undergoing such Big Events through the interaction of COVID-19, a large economic downturn, massive social unrest in many countries, and ever-worsening effects of global climate change. Previous research, mainly on HIV/AIDS, suggests that the health effects of Big Events can be profound, but are contingent: Sometimes Big Events led to enormous outbreaks of HIV and associated diseases and conditions such as injection drug use, sex trading, and tuberculosis, but in other circumstances, Big Events did not do so. This paper discusses and presents hypotheses about pathways through which the current Big Events might lead to better or worse short and long term outcomes for various health conditions and diseases; considers how pre-existing societal conditions and changing ‘pathway’ variables can influence the impact of Big Events; discusses how to measure these pathways; and suggests ways in which research and surveillance might be conducted to improve human capacity to prevent or mitigate the effects of Big Events on human health.

Longitudinal effects of social network changes on drinking outcomes for individuals with a first-time DUI

BackgroundSocial networks are important predictors of alcohol-related outcomes, especially among those with a DUI where riskier social networks are associated with increased risk of drinking and driving. Social networks are increasingly a target for intervention; however, no studies have examined and measured whether longitudinal changes in social networks are associated with reductions in impaired driving. ObjectiveThe current study first examines longitudinal changes in social networks among participants receiving services following a first-time DUI, and then examines the association between network change and drinking outcomes at 4- and 10-month follow-up. MethodsThe study surveyed a subsample of participants (N = 94) enrolled in a clinical trial of individuals randomized to cognitive behavioral therapy (CBT) or usual care (UC) on an iPad using EgoWeb 2.0—an egocentric social network data collection software—about pre-DUI and post-DUI networks and their short- and long-term drinking behaviors. ResultsParticipants were 65% male, 48% Hispanic, and an average of 32.5 years old. Overall, participants significantly reduced the proportion of network members with whom they drank from 0.41 to 0.30 (p = .001) and with whom they drank more alcohol than they wanted to from 0.15 to 0.07 (p = .0001) from two weeks prior to the DUI (measured at baseline) to 4-month follow-up. Furthermore, decreases in proportion of drinking partners over time were associated with reduced drinks per week, self-reported driving after drinking, and intentions to drive after drinking at 4-month follow-up. Participants who reported decreases in proportion of drinking partners also reported significantly less binge drinking at 10-month follow-up. Finally, increases in emotional support were associated with decreases in binge drinking at 4-month follow-up. The study found no differences in the changes in composition of networks between CBT and UC groups. ConclusionsThese results suggest that individuals receiving services in DUI programs significantly reduced risky network members over time and that these social network changes were associated with reduced drinking and other indicators of risk for DUI recidivism. Clinical interventions that target reductions in risky network members may improve outcomes for those enrolled in a DUI program.

Germs, Social Networks, and Growth

Abstract Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do differences in these patterns come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks on growth, the effect can be positive, negative, or zero. The reason is that networks diffuse both ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.

Gray-Matter Expansion of Social Brain Networks in Individuals High in Public Self-Consciousness.

Self-consciousness is a personality trait associated with an individual’s concern regarding observable (public) and unobservable (private) aspects of self. Prompted by previous functional magnetic resonance imaging (MRI) studies, we examined possible gray-matter expansions in emotion-related and default mode networks in individuals with higher public or private self-consciousness. One hundred healthy young adults answered the Japanese version of the Self-Consciousness Scale (SCS) questionnaire and underwent structural MRI. A voxel-based morphometry analysis revealed that individuals scoring higher on the public SCS showed expansions of gray matter in the emotion-related regions of the cingulate and insular cortices and in the default mode network of the precuneus and medial prefrontal cortex. In addition, these gray-matter expansions were particularly related to the trait of “concern about being evaluated by others”, which was one of the subfactors constituting public self-consciousness. Conversely, no relationship was observed between gray-matter volume in any brain regions and the private SCS scores. This is the first study showing that the personal trait of concern regarding public aspects of the self may cause long-term substantial structural changes in social brain networks.

As time went by - why is the long wave so long?

Since the end of the expansionary period after the Second War, the world economy went through a long period of mediocre growth with a major recession in 2009 and another in 2020. This period is examined following As Time Goes By, the last contribution by Chris Freeman, with the cooperation of this co-author. As a long period of readjustment after the beginning of a structural crisis is imposed by the mismatch between the capabilities of the emerging techno-economic paradigm and the socio-institutional framework, my argument is that the duration of this transition is explained by the difficult process of replacing a successful institutional setting, that which supported the post-War expansion, by the new accumulation regime that is being constituted. Instead of most of the literature on long waves, which tries to uncover some mechanics of succession of radical technological innovations, this paper addresses different questions: how does the socio-institutional adaptation proceed, and how relevant is this process to explain the length of the downswing since the turning point of the 1970s. In order to investigate such process of readjustment, the conditions for the new rule of financial accumulation are discussed, including the forms and duration of the process of selection, reproduction, and education of the elite, and changes in institutions, norms, and social networks.

Social network ties before and after retirement: a cohort study

Social networks are associated with individual’s health and well-being. Working life offers opportunities to create and maintain social networks, while retirement may change these networks. This study examined how the number of ties in social network changes across the retirement transition. The study population consisted of 2319 participants (84% women, mean age 63.2 years) from the Finnish Retirement and Aging study. Information about social network ties, including the number of ties in the inner, middle and outer circles of the social convoy model, was gathered using annual postal surveys before and after retirement. Three repeat surveys per participant covered the retirement transition and the post-retirement periods. Mean number of network ties was 21.6 before retirement, of which 5.6 were situated in the inner, 6.9 in the middle and 9.1 in the outer circle. The number of ties in the outer circle decreased by 0.67 (95% CI − 0.92, − 0.42) during the retirement transition period, but not during the post-retirement period (0.11, 95% CI − 0.33, 0.12) (interaction period * time, p = 0.006). The pattern of change in these ties did not differ by gender, occupational status, marital status, number of chronic diseases and mental health during the retirement transition period. The number of ties in the inner and middle circles overall did not decrease during these periods. The number of peripheral relationships decreased during the retirement transition but not after that, suggesting that the observed reduction is more likely to be associated with retirement rather than aging.

Beyond the limits of memory? The reliability of retrospective data in travel research

In the last decades, an emerging research interest has developed for long-term, individual travel patterns. Retrospective methods as one approach to investigate long-term behaviour on the personal level are being applied more often in travel research. Although biases and errors in retrieval are well known in cognitive psychology and memory research, they are hardly discussed in travel research. This paper focuses on three research questions: (1) What insights into the quality of retrospective methods are provided by memory research, what are its strengths and weaknesses? (2) What can we derive from the theory for the empirical work and the design of surveys? (3) What results can we transfer to the application of retrospective methods in travel research? Firstly, this paper provides an overview of the most important recall traps, such as omission, incorrect numerical and biased retrieval. As memory research has shown, various techniques can support retrieval and response behaviour regarding past behaviour, for example, the choice of event type, memory characteristics and recall aids, such as temporal and thematic landmarks, help improve accuracy. Furthermore, the design of a retrospective survey plays an important role, for example by building up an internal and thematic framework in respondents. The use of retrospective methods in travel research is categorized in the application fields of life events, changes in travel patterns, social networks and travel distances, mobility socialization, travel attitudes and needs and mobility practices. Along these categories, this paper discusses the potential, risks and opportunities to improve the use of retrospective data in travel research.

Relationship and Social Network Change in People With Impaired Social Cognition Post Right Hemisphere Stroke.

Purpose This article investigated relationship and social network change in the presence of a social cognition impairment post right hemisphere (RH) stroke. Impaired emotion perception and inferential reasoning are sources of interpersonal difficulty in people with social cognition impairment after traumatic brain injury. People with an RH stroke have also been identified as vulnerable to interpersonal relationship change. However, the influence of impairments in particular domains of communication or cognition on relationship and social network maintenance is yet to be explored. Method A multiple-case study methodology allowed for testing of theoretically developed propositions by exploring social networks and relationships within and between seven participant-proxy dyads. Purposeful recruitment was based on first-onset RH stroke and impaired social cognition, as determined by The Awareness of Social Inference Test. Results Social network size reduction (71.4%, n = 5) and interpersonal relationship change (85.7%, n = 6) were attributed to altered communication style, impairments in social cognition, and reduced insight and/or motivation. The spouse emerged as a facilitator of social engagement. Conclusion This study contributes to our understanding of the challenges experienced by people with impaired social cognition post RH stroke in maintaining relationships and their social networks.

“Digital Twins” in Education: Prospects and Reality

This article is aimed at understanding the revolutionary in meaning and “digital” in essence metamorphoses in education and the prospects for the emergence of a new educational network reality based on them. The focus of the discussion is not so much on digital innovations and transformations in education as such, but rather on the need to understand the possible prospects and results of these social network changes and modifications brought to life by the next stage of the digital revolution in education. It is proposed to include a wide range of network technologies and methodologies among the latest scientific and technological innovations taking into account the fact that the main focus of the work is on understanding the process / result of implementing the methodology of “digital twins” in education. Despite the fact that the latter is only at the beginning of its implementation in a broad educational context, the paper raises the question of the social consequences of implementing the methodology for constructing the latest network reality of “digital twins in education”, which has all the chances in the future to become a unique network of networks of “digital twins” of various social actors in education. The article discusses the managerial potential of developing and the difficulties of implementing the digital twin methodology, the expected effects of implementing the latter in education, and the socio-technological consequences of converting this technology into a new network educational reality in the Russian Federation.