The Decomposed Affiliation Exposure Model: A Network Approach to Segregating Peer Influences from Crowds and Organized Sports

Joint Latent Space Model for Social Networks with Multivariate Attributes

In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation-maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore the French financial elites based on their social networks and their career, political views and social status. We observe a division in the social circles of the French elites in accordance with the differences in their attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package "jlsm" is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf .

How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence

How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g., total number of positive responses) to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence that utilizes item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents' social network data and item-level behavior measures into a single space we call 'interaction map'. The interaction map visualizes the association between the latent homophily among respondents and their item-level behaviors, revealing differential social influence effects across item-level behaviors. We also measure overall social influence by assessing the impact of the interaction map. We evaluate the properties of the proposed approach via extensive simulation studies and demonstrate the proposed approach with a real data in the context of studying how students' friendship network influences their participation in school activities.

Recent Integrations of Latent Variable Network Modeling With Psychometric Models

The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.

Examining proximity exposure in a social network as a mechanism driving peer influence of adolescent smoking

BACKGROUND

Adolescent peers' influence on tobacco smoking is a dynamic process affected by close friends and other network peers. Although research has examined the influence of immediate friends on smoking behavior (i.e., by cohesion exposure), the influence of all peers according to closeness (i.e., proximity exposure) remains unknown. This study introduces proximity exposure as a potential driver of peer influence.

METHODS

Using the Teenage Friends and Lifestyle Study dataset, we examined 160 adolescents followed for 3 years and assessed their friendship ties and health behavior. Proximity exposure was calculated as the proportion of an individual's network peers who smoked, considering their distance from the individual. Path analysis was conducted with cross-lagged models testing the effect of proximity exposure on smoking frequency over time.

RESULTS

Among nonsmokers without cohesion exposure (n = 80), proximity exposure at year 1 was significantly associated with smoking initiation by year 3. Path analysis (n = 160) indicated that smoking at year 1 predicted cohesion exposure by year 3. When proximity exposure was included, the effect of smoking on cohesion exposure was lost. Early smoking predicted future proximity exposure. However, the predictive value of early proximity toward future smoking was stronger.

CONCLUSION

These results suggest that proximity exposure can predict smoking even among nonsmokers without direct ties to friends who smoke. In support of a peer selection hypothesis, early smoking predicted friendship formation with smokers through cohesion. Conversely, in support of a peer influence hypothesis, proximity exposure predicted smoking. Researchers may consider developing interventions that decrease proximity exposure among adolescents.

A Latent Space Network Model for Social Influence

Social network data represent interactions and relationships among groups of individuals. One aspect of social interaction is social influence, the idea that beliefs or behaviors change as a result of one's social network. The purpose of this article is to introduce a new model for social influence, the latent space model for influence, which employs latent space positions so that individuals are affected most by those who are "closest" to them in the latent space. We describe this model along with some of the contexts in which it can be used and explore the operating characteristics using a series of simulation studies. We conclude with an example of teacher advice-seeking networks to show that changes in beliefs about teaching mathematics may be attributed to network influence.

NET-EXPO: A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs

Social network analysis (SNA) concerns itself in studying network structures in relation to individuals' behavior. Individuals may be influenced by their network members in their behavior, and thus past researchers have developed computational methods that allow us to measure the extent to which individuals are exposed to members with certain behavior within one's social network, and that be correlated with their own behavior. Some of these methods include network exposure model, affiliation exposure model, and decomposed network exposure models. We developed a Gephi plugin that computes and visualizes these various kinds of network exposure models called NET-EXPO. We experimented with NET-EXPO on some social network datasets to demonstrate its pragmatic use in social network research. This plugin has the potential to equip researchers with a tool to compute network exposures in a user friendly way and simplify the process to compute and visualize the network data.

Social Network Factors and Addictive Behaviors among College Students

PURPOSE OF THE REVIEW

To provide an overview of studies within the past five years examining the impact of social network factors on addictive behaviors among college students, to discuss gaps, limitations, and controversies in the field, and to summarize with a discussion of future directions and implications for interventions.

RECENT FINDINGS

A review of 13 studies indicated that greater network exposure, centrality, reciprocated ties, and more tightly interconnected networks were associated with greater alcohol use and other addictive behaviors among college students.

SUMMARY

Greater research is needed that expands beyond alcohol use to other addictive behaviors among college students. Additionally, more studies are needed that longitudinally study the impact of changes in social networks on addictive behaviors and vice versa, as well as studies examining sociocentric (whole) networks. Social network approaches offer innovative perspectives in understanding social influences on addictive behaviors and novel intervention strategies for potentially reducing addictive behaviors among college students.

Gangs, clubs, and alcohol: The effect of organizational membership on adolescent drinking behavior

How does adolescent organizational membership in general, and simultaneous membership in distinct types of organizations in particular, impact drinking behavior? While past studies have focused either on the learning effect of involvement with gangs or on the constraining influence of conventional organizations on adolescent problem behavior, we explore the possibility that conventional school clubs can serve as socializing opportunities for existing gang members to engage in drinking behavior with non-gang club members. Using the Add Health data, we show that gang members drink more often, and engage in more binge drinking, than non-members. More importantly, individuals who are members of both gangs and school clubs drink alcohol at greater levels than those who are solely involved in gangs. In addition, non-gang adolescents who are co-members with gang members in the same school club are more likely to drink alcohol than non-members. This result has important implications for understanding the role of organizations in adolescent behavior and suggests that the study of delinquent behaviors would benefit from devoting more attention to individuals who bridge distinct types of organizations.

Multiplex congruity: friendship networks and perceived popularity as correlates of adolescent alcohol use

Adolescents interact with their peers in multiple social settings and form various types of peer relationships that affect drinking behavior. Friendship and popularity perceptions constitute critical relationships during adolescence. These two relations are commonly measured by asking students to name their friends, and this network is used to construct drinking exposure and peer status variables. This study takes a multiplex network approach by examining the congruity between friendships and popularity as correlates of adolescent drinking. Using data on friendship and popularity nominations among high school adolescents in Los Angeles, California (N = 1707; five schools), we examined the associations between an adolescent's drinking and drinking by (a) their friends only; (b) multiplexed friendships, friends also perceived as popular; and (c) congruent, multiplexed-friends, close friends perceived as popular. Logistic regression results indicated that friend-only drinking, but not multiplexed-friend drinking, was significantly associated with self-drinking (AOR = 3.51, p < 0.05). However, congruent, multiplexed-friend drinking also was associated with self-drinking (AOR = 3.10, p < 0.05). This study provides insight into how adolescent health behavior is predicated on the multiplexed nature of peer relationships. The results have implications for the design of health promotion interventions for adolescent drinking.