Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and would suggest strategies for interventions seeking to reduce risky-prescribing (e.g., strategies to directly reduce risky prescribing might be most effective if applied as group interventions to risky prescribing physicians connected through the network and the connections between these physicians could be targeted by tie dissolution interventions as an indirect way of reducing risky prescribing). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques-groups of actors that are fully connected to each other-such as closed triangles in the case of three actors), this would further strengthen the case for targeting groups of physicians involved in risky prescribing and the network connections between them for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology may be applied, adapted or generalized to study homophily and its generalizations on other network and attribute combinations involving analogous shared-patient networks and more generally using other kinds of network data underlying other kinds of social phenomena.

The prototypical UK blood donor, homophily and blood donation: Blood donors are like you, not me.

BACKGROUND AND OBJECTIVES: Homophily represents the extent to which people feel others are like them and encourages the uptake of activities they feel people like them do. Currently, there are no data on blood donor homophily with respect to (i) people's representation of the average prototypical UK blood donor and (ii) the degree of homophily with this prototype for current donors, non-donors, groups blood services wish to encourage (ethnic minorities), those who are now eligible following policy changes (e.g., men-who-have-sex-with-men: MSM) and recipients. We aim to fill these gaps in knowledge. MATERIALS AND METHODS: We surveyed the UK general population MSM, long-term blood recipients, current donors, non-donors and ethnic minorities (n = 785) to assess perceptions of the prototypical donor in terms of ethnicity, age, gender, social class, educational level and political ideology. Homophily was indexed with respect to age, gender and ethnicity. RESULTS: The prototypical UK blood donor is perceived as White, middle-aged, middle-class, college-level educated and left-wing. Current donors and MSM are more homophilous with this prototype, whereas recipients and ethnic minorities have the lowest homophily. Higher levels of homophily are associated with an increased likelihood of committing to donate. CONCLUSION: The prototype of the UK donor defined this as a White activity. This, in part, may explain why ethnic minorities are less likely to be donors. As well as traditional recruitment strategies, blood services need to consider broader structural changes such as the ethnic diversity of staff and co-designing donor spaces with local communities.

Effect of homophily on coupled behavior-disease dynamics near a tipping point.

Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.

PathMLP: Smooth path towards high-order homophily.

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, from the observation of heterophilous data, we notice that certain high-order information exhibits higher homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: (1) over-smoothing due to excessive model depth and propagation times; (2) high-order information is not fully utilized; (3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency. The source code will be available in https://github.com/Graph4Sec-Team/PathMLP.

Vaccination homophily in ego contact networks during the COVID-19 pandemic.

Vaccine hesitancy is an inevitable risk for societies as it contributes to outbreaks of diseases. Prior research suggests that vaccination decisions of individuals tend to spread within social networks, resulting in a tendency to vaccination homophily. The clustering of individuals resistant to vaccination can substantially make the threshold necessary to achieve herd immunity harder to reach. In this study, we examined the extent of vaccination homophily among social contacts and its association with vaccine uptake during the COVID-19 pandemic in Hungary using a contact diary approach in two cross-sectional surveys. The results indicate strong clustering among both vaccinated and unvaccinated groups. The most powerful predictor of vaccine uptake was the perceived vaccination rate within the egos' social contact network. Vaccination homophily and the role of the interpersonal contact network in vaccine uptake were particularly pronounced in the networks of close relationships, including family, kinship, and strong social ties of the ego. Our findings have important implications for understanding COVID-19 spread dynamics by showing that the strong clustering of unvaccinated individuals posed a great risk in preventing the spread of the disease.

The evolutionary cost of homophily: social stratification facilitates stable variant coexistence and increased rates of evolution in host-associated pathogens.

Coexistence of multiple strains of a pathogen in a host population can present significant challenges to vaccine development or treatment efficacy. Here we discuss a novel mechanism that can increase rates of long-lived strain polymorphism, rooted in the presence of social structure in a host population. We show that social preference of interaction, in conjunction with differences in immunity between host subgroups, can exert varying selection pressure on pathogen strains, creating a balancing mechanism that supports stable viral coexistence, independent of other known mechanisms. We use population genetic models to study rates of pathogen heterozygosity as a function of population size, host population composition, mutant strain fitness differences and host social preferences of interaction. We also show that even small periodic epochs of host population stratification can lead to elevated strain coexistence. These results are robust to varying social preferences of interaction, overall differences in strain fitnesses, and spatial heterogeneity in host population composition. Our results highlight the role of host population social stratification in increasing rates of pathogen strain diversity, with effects that should be considered when designing policies or treatments with a long-term view of curbing pathogen evolution.

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs.

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, they often overlook the other kind of widespread heterophilic graphs, in which adjacent nodes tend to have different classes or dissimilar features. Recent methods attempt to address heterophilic graphs from the graph spatial domain, which try to aggregate more similar nodes or prevent dissimilar nodes with negative weights. However, they may neglect valuable heterophilic information or extract heterophilic information ineffectively, which could cause poor performance of downstream tasks on heterophilic graphs, including node classification and graph classification, etc. Hence, a novel framework named GARN is proposed to effectively extract both homophilic and heterophilic information. First, we analyze the shortcomings of most GNNs in tackling heterophilic graphs from the perspective of graph spectral and spatial theory. Then, motivated by these analyses, a Graph Aggregating-Repelling Convolution (GARC) mechanism is designed with the objective of fusing both low-pass and high-pass graph filters. Technically, it learns positive attention weights as a low-pass filter to aggregate similar adjacent nodes, and learns negative attention weights as a high-pass filter to repel dissimilar adjacent nodes. A learnable integration weight is used to adaptively fuse these two filters and balance the proportion of the learned positive and negative weights, which could control our GARC to evolve into different types of graph filters and prevent it from over-relying on high intra-class similarity. Finally, a framework named GARN is established by simply stacking several layers of GARC to evaluate its graph representation learning ability on both the node classification and image-converted graph classification tasks. Extensive experiments conducted on multiple homophilic and heterophilic graphs and complex real-world image-converted graphs indicate the effectiveness of our proposed framework and mechanism over several representative GNN baselines.

Applying Social Network Theory to Vaping in High School: Implications for Person-Centered Intervention.

BACKGROUND: Peer influence on risky behavior is particularly potent in adolescence and varies by gender. Smoking prevention programs focused on peer-group leaders have shown great promise, and a social influence model has proven effective in understanding adult smoking networks but has not been applied to adolescent vaping until 2023. This work aims to apply a social influence model to analyze vaping by gender in a high school network. METHODS: A high school's student body was emailed an online survey asking for gender, age, grade level, vape status, and the names of three friends. Custom Java and MATLAB scripts were written to create a directed graph, compute centrality measures, and perform Fisher's exact tests to compare centrality measures by demographic variables and vape status. RESULTS: Of 192 students in the school, 102 students responded. Students who vape were in closer-knit friend groups than students who do not vape (p < .05). Compared to males who vape, females who vape had more social ties to other students who vape, exhibiting greater homophily (p < .01). Compared to females who do not vape, females who vape were in closer-knit friend groups (p < .05) and had more ties to other students who vape (p < .01). CONCLUSION: Differences in vaping by social connectedness and gender necessitate school and state policies incorporating the social aspect of vaping in public health initiatives. Large-scale research should determine if trends can be generalized across student bodies, and more granular studies should investigate differences in motivations and social influence by demographic variables to individualize cessation strategies.

Sisterhood predicts similar neural processing of a film.

Relationships between humans are essential for how we see the world. Using fMRI, we explored the neural basis of homophily, a sociological concept that describes the tendency to bond with similar others. Our comparison of brain activity between sisters, friends and acquaintances while they watched a movie, indicate that sisters' brain activity is more similar than that of friends and friends' activity is more similar than that of acquaintances. The increased similarity in brain activity measured as inter-subject correlation (ISC) was found both in higher-order brain areas including the default-mode network (DMN) and sensory areas. Increased ISC could not be explained by genetic relation between sisters neither by similarities in eye-movements, emotional experiences, and physiological activity. Our findings shed light on the neural basis of homophily by revealing that similarity in brain activity in the DMN and sensory areas is the stronger the closer is the relationship between the people.

Suicide awareness homophily in adolescent peer support networks: A Swiss cross-sectional social network analysis.

Objective: Suicide awareness, encompassing knowledge, attitudes, and behaviors related to suicide, plays a critical role in primary suicide prevention, especially among adolescents. However, little is known about how perceived suicide awareness is apparent in peer support networks in this population. This study examined the presence of suicide awareness homophily in adolescent peer support networks. We also explored other patterns of homophily and identified factors associated with the in-degree popularity of adolescents. Methods: We used baseline data from a non-randomized, cluster-controlled trial assessing the effectiveness of a universal suicide prevention intervention in Swiss secondary schools (n = 194). We assessed perceived suicide awareness, support networks (including in-degree popularity, i.e., receiving a high number of nominations as a supportive peer), and other covariates. Data were analyzed using social network analyses. Results: We found evidence of suicide awareness homophily in peer support networks, where adolescents with high suicide awareness were more likely to connect with peers having high suicide awareness (p < .001). The same applied to those with low suicide awareness (p < .001). Age also emerged as a significant homophily factor. Girls (p = .024) and adolescents with high instrumental social support (p = .008) were more likely to be popular in peer support networks. Conclusions: This study highlighted the homophily of suicide awareness in peer support networks and the need to focus on strengthening peer support networks and promoting suicide awareness in adolescents, particularly for those with low suicide awareness. Future suicide prevention programs, including peer-led interventions, should consider these findings to better target vulnerable subgroups and reduce suicide-related disparities.

Estimating Partnership Duration among MSM in Belgium-A Modeling Study.

Mathematical modeling is widely used for describing infection transmission and evaluating interventions. The lack of reliable social parameters in the literature has been mentioned by many modeling studies, leading to limitations in the validity and interpretation of the results. Using data from the European MSM Internet survey 2017, we developed a network model to describe sex acts among MSM in Belgium. The model simulates daily sex acts among steady, persistent casual and one-off partners in a population of 10,000 MSM, grouped as low- or high-activity by using three different definitions. Model calibration was used to estimate partnership duration and homophily rates to match the distribution of cumulative sex partners over 12 months. We estimated an average duration between 1065 and 1409 days for steady partnerships, 4-6 and 251-299 days for assortative high- and low-activity individuals and 8-13 days for disassortative persistent casual partnerships, respectively, varying across the three definitions. High-quality data on social network and behavioral parameters are scarce in the literature. Our study addresses this lack of information by providing a method to estimate crucial parameters for network specification.

Blocking of counter-partisan accounts drives political assortment on Twitter.

There is strong political assortment of Americans on social media networks. This is typically attributed to preferential tie formation (i.e. homophily) among those with shared partisanship. Here, we demonstrate an additional factor beyond homophily driving assorted networks: preferential prevention of social ties. In two field experiments on Twitter, we created human-looking bot accounts that identified as Democrats or Republicans, and then randomly assigned users to be followed by one of these accounts. In addition to preferentially following-back copartisans, we found that users were 12 times more likely to block counter-partisan accounts compared to copartisan accounts in the first experiment, and 4 times more likely to block counter-partisan accounts relative to a neutral account or a copartisan account in the second experiment. We then replicated these findings in a survey experiment and found evidence of a key motivation for blocking: wanting to avoid seeing any content posted by the blocked user. Additionally, we found that Democrats preferentially blocked counter-partisans more than Republicans, and that this asymmetry was likely due to blocking accounts who post low-quality or politically slanted content (rather than an asymmetry in identity-based blocking). Our results demonstrate that preferential blocking of counter-partisans is an important phenomenon driving political assortment on social media.

Examining the social and behavioral dynamics of substance use in a longitudinal network study in rural Appalachia.

BACKGROUND: Prior studies have shown that individuals and their peers often have similar substance use behaviors, but the mechanisms driving these similarities - particularly in rural settings, are not well understood. The primary objectives of this analysis are to (1) identify factors that contribute to relationship turnover and maintenance within a rural network of persons who use drugs (PWUD), (2) determine whether assimilation and/or homophily shape participants use of injection drugs, heroin, and stimulants (methamphetamine and cocaine), and (3) assess the extent that these mechanisms influence networks ties and/or behaviors and whether these effects vary across time. METHODS: Sociometric network data were collected from a cohort of PWUD in rural Eastern Kentucky at baseline (2008-2010) and at four follow-up visits conducted approximately semiannually. Stochastic actor-oriented models (SAOMS) were used to model network structure and participant behaviors as jointly dependent variables and to identify characteristics associated with the maintenance, dissolution, and formation of network ties and changes in drug use behaviors. RESULTS: Findings suggest (1) greater network stability over time for reciprocal and transitive relationships, (2) both homophily and assimilation played a greater role in shaping injection drug use (IDU) initiation and cessation than they did in shaping heroin and stimulant use, and (3) the importance of these mechanisms appeared consistent over time. CONCLUSION: Given the stability of particular network structures and evidence of both homophily and assimilation with respect to drug-use behaviors, interventions that leverage social networks could be used to motivate health-promoting behaviors.

Friendship Homophily Trajectories among Asian American Youth from High School to College.

Friendship racial homophily, the tendency to form friendships with individuals who share the same racial background, is a normative developmental phenomenon that holds particular significance for youth of color in a racialized society. Yet, there exists a paucity of longitudinal research elucidating the stability and change of friendship racial composition across developmental span. This study aimed to examine the friendship racial homophily trajectories over a six-year period encompassing four time points. The sample comprised 437 Asian American youth (MAge = 16.52, SDAge = 1.36, 53% female), with 197 Filipino and 240 Korean participants. Using logistic multilevel modeling analyses, it was found that both Filipino and Korean American youth demonstrated an increase in friendship racial homophily from high school to college, but that Filipino youth overall reported lower levels of racial homophily compared to their Korean counterparts. The study findings also pinpointed several influential factors impacting these trajectories, including proficiency in heritage languages, ethnic identity, and encounters with racial discrimination from both White Americans and other People of Color. These results highlight the continuous evolution of friendship racial composition from high school to college and emphasize the crucial role of ethnic identity and experiences of discrimination in influencing these dynamics, with ethnic identity exerting more enduring effects and experiences of discrimination showing more situational impacts on the levels of racial homophily.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

Variety Is the Spice of Life: Diverse Social Networks Are Associated With Social Cohesion and Well-Being.

Both homophily and heterophily are observed in humans. Homophily reinforces homogeneous social networks, and heterophily creates new experiences and collaborations. However, at the extremes, high levels of homophily can cultivate prejudice toward out-groups, whereas high levels of heterophily can weaken in-group support. Using data from 24,726 adults (M = 46 years; selected from 10,398 English neighborhoods) and the composition of their social networks based on age, ethnicity, income, and education, we tested the hypothesis that a middle ground between homophily and heterophily could be the most beneficial for individuals. We found that network homophily, mediated by perceived social cohesion, is associated with higher levels of subjective well-being but that there are diminishing returns, because at a certain point increasing network homophily is associated with lower social cohesion and, in turn, lower subjective well-being. Our results suggest that building diverse social networks provides benefits that cannot be attained by homogeneous networks.

Higher-order homophily on simplicial complexes.

Higher-order network models are becoming increasingly relevant for their ability to explicitly capture interactions between three or more entities in a complex system at once. In this paper, we study homophily, the tendency for alike individuals to form connections, as it pertains to higher-order interactions. We find that straightforward extensions of classical homophily measures to interactions of size 3 and larger are often inflated by homophily present in pairwise interactions. This inflation can even hide the presence of anti-homophily in higher-order interactions. Hence, we develop a structural measure of homophily, simplicial homophily, which decouples homophily in pairwise interactions from that of higher-order interactions. The definition applies when the network can be modeled as a simplicial complex, a mathematical abstraction which makes a closure assumption that for any higher-order relationship in the network, all corresponding subsets of that relationship occur in the data. Whereas previous work has used this closure assumption to develop a rich theory in algebraic topology, here we use the assumption to make empirical comparisons between interactions of different sizes. The simplicial homophily measure is validated theoretically using an extension of a stochastic block model for simplicial complexes and empirically in large-scale experiments across 16 datasets. We further find that simplicial homophily can be used to identify when node features are valuable for higher-order link prediction. Ultimately, this highlights a subtlety in studying node features in higher-order networks, as measures defined on groups of size k can inherit features described by interactions of size [Formula: see text].

Homophily modulates double descent generalization in graph convolution networks.

Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understood. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of "transductive" double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets.

The role of vaccine status homophily in the COVID-19 pandemic: a cross-sectional survey with modelling.

BACKGROUND: Vaccine homophily describes non-heterogeneous vaccine uptake within contact networks. This study was performed to determine observable patterns of vaccine homophily, as well as the impact of vaccine homophily on disease transmission within and between vaccination groups under conditions of high and low vaccine efficacy. METHODS: Residents of British Columbia, Canada, aged ≥ 16 years, were recruited via online advertisements between February and March 2022, and provided information about vaccination status, perceived vaccination status of household and non-household contacts, compliance with COVID-19 prevention guidelines, and history of COVID-19. A deterministic mathematical model was used to assess transmission dynamics between vaccine status groups under conditions of high and low vaccine efficacy. RESULTS: Vaccine homophily was observed among those with 0, 2, or 3 doses of the vaccine. Greater homophily was observed among those who had more doses of the vaccine (p < 0.0001). Those with fewer vaccine doses had larger contact networks (p < 0.0001), were more likely to report prior COVID-19 (p < 0.0001), and reported lower compliance with COVID-19 prevention guidelines (p < 0.0001). Mathematical modelling showed that vaccine homophily plays a considerable role in epidemic growth under conditions of high and low vaccine efficacy. Furthermore, vaccine homophily contributes to a high force of infection among unvaccinated individuals under conditions of high vaccine efficacy, as well as to an elevated force of infection from unvaccinated to suboptimally vaccinated individuals under conditions of low vaccine efficacy. INTERPRETATION: The uneven uptake of COVID-19 vaccines and the nature of the contact network in the population play important roles in shaping COVID-19 transmission dynamics.

Homophily, selection, and choice in segregation models.

Schelling's 1971 work on the dynamics of segregation showed that even a small degree of homophily, the desire to live among like neighbors, can lead to a starkly segregated population. One of the driving factors for this result is that the notion of homophily used is based on group identities that are exogenous and immutable. In contrast, we consider a homophily that arises from the desire to be with neighbors who are behaviorally similar, not necessarily those who have the same group identity. The distinction matters because behaviors are neither exogenous nor immutable but choices that can change as individuals adapt to their neighborhoods. We show that in such an environment, integration rather than segregation is the typical outcome. However, the tendency toward adaptation and integration can be impeded when economic frictions in the form of income inequality and housing cost are present.