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The casual effect of relational mobility on integration of social networks: An agent-based modeling approach.

ABSTRACT: Despite converging evidence for the importance of relational mobility on shaping people's social experiences, previous work suggested mixed findings for its influence on the structure of sociocentric networks, which lays the basis for the development of all types of social relationships. Additionally, as it is timely and economically intractable to administer such longitudinal experiments in real-life settings, most previous work mainly relied on cross-sectional correlation analyses and provided limited causal evidence. The current research used an agent-based modeling approach to examine whether higher relational mobility (i.e., the number of opportunities to meet new people) would promote integration among social networks over time. Using parameters derived from survey data, we simulated how the integration of sociocentric social networks evolves under different levels of relational mobility. Based on the data of three network structural indicators, including modularity, global efficiency, and standard deviation of nodal betweenness, we obtained causal evidence supporting that higher relational mobility promotes greater network integration. These findings highlight the power of socioecological demands on our social experiences.

Supplementary information

The online version contains supplementary material available at 10.1007/s12144-022-03130-x.

SUBMITTER: Li LMW

PROVIDER: S-EPMC9170874 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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The casual effect of relational mobility on integration of social networks: An agent-based modeling approach.

Li Liman Man Wai LMW Wang Shengyuan S Lin Ying Y

Current psychology (New Brunswick, N.J.) 20220607

Despite converging evidence for the importance of relational mobility on shaping people's social experiences, previous work suggested mixed findings for its influence on the structure of sociocentric networks, which lays the basis for the development of all types of social relationships. Additionally, as it is timely and economically intractable to administer such longitudinal experiments in real-life settings, most previous work mainly relied on cross-sectional correlation analyses and provided ...[more]

PMID: 35693837

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Project description:Note: Data Use Restrictions: <ul> <li>The data must only be used for genetic research projects solely in the area of health-related social-network research.</li> </ul> This study compiles detailed information on family and social ties linked to participants in the offspring cohort of the Framingham Heart Study. The investigators took computerized information from self-report data used by the FHS for over 30 years to facilitate health exam scheduling. These tracking sheets asked participants to identify people close to them, which were assumed by the investigators to indicate a social tie. Individuals are listed as "egos" (if the ties are from their perspective) and/or "alters" (if they are linked as a friend or family members to one of the other participants). All egos were FHS offspring participants while alters could be FHS participants in any cohort or non-participants. This information was combined with internal FHS pedigree data on family ties to list family member alters (as well as the nature of their relationship). The resultant dataset therefore includes each ego-alter tie, the nature of their relationship and the start and end dates for their ties for each of seven waves for the offspring study. Due to privacy concerns, all exam dates for individuals are listed relative to the initial FHS exam date. The study investigators chose a random, confidential date, to which all initial exam dates are linked (by number of months from random date to initial exam date). With this information, researchers can have access to the temporal relationships among participants' exam dates while remaining unaware of the actual exam dates for individual participants. During each clinic exam cycle, the participants undergo a detailed examination including physical examination, medical history, laboratory testing, and electrocardiogram. Over the years, other tests (that may not be performed at every exam cycle) have included pulmonary function, lifestyle, physical function, cognitive function questionnaires, and various noninvasive cardiovascular tests including echocardiograms. This study involves phenotypic data from these exams including (when available) basic body measurements (height, weight, blood pressure), laboratory values (blood sugar levels, LDL levels), information on smoking and alcohol use, and tests of depression and cognitive functioning. Important links to apply for individual-level data <ol> <li><a href="http://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?view_pdf&stacc=phs000153.v3.p3" target="_blank">Data Use Certification Requirements (DUC)</a></li> <li><a href="GetPdf.cgi?id=phd001183.1" target="_blank">Instructions to Request Authorized Access</a></li> <li><a href="http://view.ncbi.nlm.nih.gov/dbgap-controlled" target="_blank">Apply here for controlled access to individual level data</a></li> <li><a href="GetPdf.cgi?id=phd000317" target="_blank">Participant Protection Policy FAQ</a></li> </ol> In 1948, researchers recruited men and women from the town of Framingham, Massachusetts, beginning the first round of extensive physical examinations and lifestyle interviews that would later be analyzed for common patterns related to CVD development. Initially, the Framingham Heart Study enrolled 5,209 men and women from the Framingham area who were between the ages of 28 and 62 years. Beginning in 1971, the Framingham Heart Study enrolled 5,124 men and women, who were either offspring of the original cohort or spouses of those offspring. In 2002, 4,095 third generation participants (men and women) were enrolled.

Dataset Information

The casual effect of relational mobility on integration of social networks: An agent-based modeling approach.

Supplementary information

Publications

The casual effect of relational mobility on integration of social networks: An agent-based modeling approach.

Similar Datasets

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