Individuals are motivated to increase their social status. To succeed in this pursuit, people must track information about others in their social sphere, monitor group norms, and adjust their behavior strategically. This study employed functional MRI and ecological momentary assessment methods in a sample of 92 college students belonging to 9 social groups to elucidate the neural mechanisms underlying these processes and their relationship to conformity in the context of alcohol use. When young adults passively looked at faces of their real-life social group peers, brain systems implicated in valuation and social cognition spontaneously tracked information about the popularity and leadership status of the social targets in an interdependent manner. Individual differences in these neural valuations were systematically associated with varying levels of conformity. Students who had stronger responses to faces of peers with relatively higher popularity and leadership status than themselves in one key valuation brain region, the ventromedial prefrontal cortex (vmPFC), were more likely to align their drinking behavior with their groups' norms in everyday life. These results provide evidence for how brain systems involved in valuation and social cognition flexibly track information about peers' popularity and leadership status in real-life social groups and contribute to a growing literature on the neural mechanisms through which social comparison processes shape conformity. Our study highlights the vmPFC as a central hub that spontaneously tracks status differences between the self and peers and uses this information to guide behavior to match group norms.

Digital interventions can change behaviors like alcohol use, but effectiveness varies widely across individuals. Accurately identifying non-responders—i.e., those least (vs. most) likely to change their behavior—before intervention delivery is difficult. Individual intervention effectiveness predictions from prior studies perform only slightly above chance (e.g., AUC ≈0.60; balanced accuracy ≈0.60). We present a novel approach integrating multimodal data across theory-driven domains—including psychological assessments, social network data, and neural responses to alcohol cues—to make ex-ante predictions about the effectiveness of smartphone-delivered alcohol interventions targeting psychological distancing in young adults (Study 1: N = 67; Study 2: N = 114). Demonstrating the feasibility of this approach, random forest models predicted individual differences in intervention effectiveness (Study 1: balanced accuracy = 0.71, 95% CI: 0.69–0.73, p = .020; AUC = 0.87, 95% CI: 0.85–0.88, p = .020) and replicated in a an external test sample (Study 2, balanced accuracy = 0.68; AUC = 0.68, 95% CI: 0.54–0.82), meeting clinical-utility thresholds from prior digital health studies (balanced accuracy = 0.67; correctly classifying (non)responders 67% of the time). Interventions were most effective for participants who perceived their peers as moderate but frequent drinkers. Peer drinking perceptions may serve as a low-burden indicator to support early identification of non-responders in preventive alcohol interventions among young adults. Future work can apply and extend the multimodal approach developed here for adaptive tailoring of digital behavior change interventions in real-world settings.

Developing interventions to change health behaviors—especially those targeting cross-cutting health risk factors like alcohol use—is a public health priority. In this study, we used a translational neuroscience approach to evaluate the underlying mechanisms and individual differences in a mindful distancing intervention designed to reduce alcohol consumption among college students. We combined functional neuroimaging and machine learning to develop a brain-based predictive model (a “neural signature”) of mindful distancing. This model allowed us to track moment-to-moment variation in how participants implemented the strategy, as well as differences between individuals. Students completed a mindful distancing task involving alcohol cues during fMRI scanning. They then completed a 28-day, smartphone-based, experience sampling intervention. In the laboratory, mindfully attending to alcohol decreased craving, particularly among people who more strongly expressed the mindful distancing signature. In daily life, the mindful distancing intervention increased mindful responses to alcohol and decreased subsequent alcohol consumption through two distinct pathways: mindful responses directly influenced alcohol consumption and indirectly influenced it by reducing cravings for alcohol. Individuals with stronger expression of the neural signature experienced the greatest benefits from the intervention. These findings extend theoretical models of how mindfulness-based emotion regulation strategies impact alcohol use in emerging adults without alcohol use disorders. They also demonstrate the potential of using neural signatures to evaluate health behavior change interventions within a translational neuroscience framework.

Close social relationships are critical for emotional well-being. The COVID-19 pandemic severely disrupted in-person contact with friends, particularly among young adults, for whom friendships support key developmental goals. In a longitudinal study of U.S. college students (N = 205; 10,088 observations), we examined how close friendship networks related to emotional well-being during the early months of the pandemic (May-October 2020). Leveraging prepandemic social network data and 28 days of ecological momentary assessments of affect and social interactions, we found that students with more close college friends reported higher positive affect and lower negative affect in daily life, even while physically separated from those friends. These individuals were buffered from the emotional toll of pandemic-related stressors, a pattern not explained by personality, interaction frequency, or living conditions. Rather, participants with more close friends experienced higher quality online interactions. Additionally, personal disclosures, whether in-person or online, were consistently associated with greater feelings of closeness. Notably, individuals with fewer close friends showed the largest boost in closeness following partner disclosures, suggesting that emotional sharing may play a compensatory role for those with limited social ties. These findings illustrate how friendships can continue to shape affective experiences from afar and highlight disclosure as a key mechanism through which closeness and its emotional benefits can be cultivated. Integrating social network structure, daily affect, and interaction-level processes, this work advances affective science by providing evidence of how the social regulation of emotion extends beyond physical proximity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The present study investigated between-person differences in daily positive emotion dynamics and their associations with flourishing across two studies (Study 1: n=244, Study 2: n=265). Three between-person indices of daily positive emotion dynamics were created: average intensity, variability, and inertia. Using latent profile analysis, a data-driven technique that identifies subgroups (referred to as profiles) within a population, four common ways in which these three emotion dynamics cluster at the person level were identified. Testing for associations between flourishing and the observed profiles of emotion dynamics revealed that people with high levels of positive emotion that were stable over time were highest in flourishing, followed by low-intensity but variable positive emotions, followed by individuals with low-intensity positive emotions. By considering how three key emotion dynamic indices cluster within individuals, we find that understanding both the average intensity and the extent of stability in daily positive emotion is necessary for understanding flourishing.

A good match between clinicians and clients can substantially impact psychotherapy outcomes. No proposed matching methodology, however, accounts for the multitude of relevant variables, such as complaint type, demographics, life experiences, and personality, as well as for practical concerns such as therapist availability. Prior work has often focused on single-variable models and average effects across populations, and studies that take more multifaceted approaches base their results on simulations or carefully constructed situations. To nuance our matching, we estimated the complex effect of therapists on client outcomes using machine learning trained on high dimensional data from CCAPS and OQ-45 surveys from 2014 to 2019. We used these predictions to produce constrained matches, optimizing outcomes for cohorts of clients. Using our method, called Matching Assistant for Therapists and Client Health (MATCH), clients assigned would experience better outcomes on average with minimal impact on their wait-time and few administrative changes.

Many empirical networks originate from correlational data, arising in domains as diverse as psychology, neuroscience, genomics, microbiology, finance, and climate science. Specialized algorithms and theory have been developed in different application domains for working with such networks, as well as in statistics, network science, and computer science, often with limited communication between practitioners in different fields. This leaves significant room for cross-pollination across disciplines. A central challenge is that it is not always clear how to best transform correlation matrix data into networks for the application at hand, and probably the most widespread method, i.e., thresholding on the correlation value to create either unweighted or weighted networks, suffers from multiple problems. In this article, we review various methods of constructing and analyzing correlation networks, ranging from thresholding and its improvements to weighted networks, regularization, dynamic correlation networks, threshold-free approaches, comparison with null models, and more. Finally, we propose and discuss recommended practices and a variety of key open questions currently confronting this field.

Studies on college drinking-a behavior associated with health risks and reduced productivity-often rely on broadly defined peer or friendship networks. Yet, friendship networks can be further divided into more specific relational types, and the associations between such diverse social ties and drinking ties remain poorly understood. This study adopts a multilayer network framework to conduct a fine-grained examination of ten distinct types of social networks (i.e., layers)-including friendship, leadership, emotional support, and perceived drinking-by analyzing their structural similarity across three levels (node, link, and triad) and along a relationship-axis spectrum. We cluster networks based on these multi-level similarities, identifying three primary clusters (Affiliation, Leadership, and Drinking) and assess whether these structurally coherent clusters contribute to improved link prediction performance in perceived drinking networks. Both k-means clustering based on multi-level structural similarity and projection-based analysis along the hierarchical-horizontal spectrum revealed that perceived drinking nominations are structurally closest to leadership layers, followed by emotional support layers within the Affiliation cluster. Assessing whether these functional clusters can improve link prediction in perceived drinking networks, we find that layers within the same cluster yield predictive performance close to that of models using all layers. Notably, emotional support layers, which may reflect their structural proximity and distributed connectivity, offer the highest link prediction accuracy for drinking ties. Taken together, these findings demonstrate that coherent clusters of fine-grained, functionally and structurally aligned social layers facilitate more efficient inference in sparse or partially observed drinking-related networks. This, in turn, highlights their potential utility in predicting and preventing health-risk behaviors-such as alcohol use-that are typically difficult to observe or measure directly, and clarifies the types of relationships most strongly associated with such behaviors.

Social interactions are fundamental to human well-being. Much of what people invest time, energy, and money in revolves around connecting with others. Yet many people still feel lonely, increasing their risk for poor sleep quality, depression, and mortality. Identifying underlying mechanisms of loneliness is key to developing efficient interventions. Here, we used neuroimaging to investigate how multivariate patterns of functional connectivity between and within brain systems relate to loneliness. We developed a neural signature of loneliness from distributed patterns of resting-state functional connectivity in the Human Connectome Project (HCP) dataset. We then demonstrate the generalizability of this neural signature in predicting individual differences in loneliness in an independent sample of young adult students. Our results reveal that functional connectivity between sensory systems and higher-order process networks (frontoparietal control and default networks) is a key feature of how loneliness is instantiated in the brain. Finally, this neural signature appears to be specific for loneliness, as it was not predictive of other closely related measures of social connection or negative affect. Overall, this work offers a neural marker that could be used across new datasets, and suggests key brain systems to target with future interventions.

Conversations shape future behaviors, particularly among young adults. However, young adults vary widely in their susceptibility to peer influence. What neural processes relate to this susceptibility? We examined whether activity in brain regions associated with social rewards and making sense of others’ minds relates to a common health behavior—drinking, following conversations about alcohol. We studied ten social groups of college students (N = 104 students; 4760 total observations) across two university campuses. We collected whole-brain fMRI data while participants viewed photographs of peers with whom they tended to drink at varying frequencies. Next, using ecological momentary assessment, we tracked alcohol conversations and drinking twice daily for 28 days. On average, talking about alcohol was associated with a higher likelihood of next-day drinking. Controlling for baseline drinking, participants who responded more strongly to peers with whom they drank alcohol more frequently—in brain regions associated with social rewards and mentalizing—showed a stronger, positive association between alcohol conversations and next-day drinking. Conversely, stronger neural responses to peers with whom they drank less frequently decoupled associations between alcohol conversations and next-day drinking. We conceptually replicate prior findings linking conversations and drinking in an observational, longitudinal setting and provide new evidence that neural responses to peers moderate links between alcohol conversations and drinking behavior among young adults.

Influence propagation in social networks is a central problem in modern social network analysis, with important societal applications in politics and advertising. A large body of work has focused on cascading models, viral marketing, and finite-horizon diffusion. There is, however, a need for more developed, mathematically principled adversarial models, in which multiple, opposed actors strategically select nodes whose influence will maximally sway the crowd to their point of view. In the present work, we develop and analyze such a model based on harmonic functions and linear diffusion. Our general problem is known to be NP-hard and that the objective function is monotone and submodular; consequently, we can greedily approximate the solution within a constant factor. Introducing and analyzing a convex relaxation, we show that the problem can be approximately solved using smooth optimization methods. We illustrate the effectiveness of our approach on a variety of example networks.

We analyze the performance of graph neural network (GNN) architectures from the perspective of random graph theory. Our approach promises to complement existing lenses on GNN analysis, such as combinatorial expressive power and worst-case adversarial analysis, by connecting the performance of GNNs to typical-case properties of the training data. First, we theoretically characterize the nodewise accuracy of one- and two-layer GCNs relative to the contextual stochastic block model (cSBM) and related models. We additionally prove that GCNs cannot beat linear models under certain circumstances. Second, we numerically map the recoverability thresholds, in terms of accuracy, of four diverse GNN architectures (GCN, GAT, SAGE, and Graph Transformer) under a variety of assumptions about the data. Sample results of this second analysis include: heavy-tailed degree distributions enhance GNN performance, GNNs can work well on strongly heterophilous graphs, and SAGE and Graph Transformer can perform well on arbitrarily noisy edge data, but no architecture handled sufficiently noisy feature data well. Finally, we show how both specific higher-order structures in synthetic data and the mix of empirical structures in real data have dramatic effects (usually negative) on GNN performance.

We provide a rearrangement based algorithm for detection of subgraphs of k vertices with long escape times for directed or undirected networks that is not combinatorially complex to compute. Complementing other notions of densest subgraphs and graph cuts, our method is based on the mean hitting time required for a random walker to leave a designated set and hit the complement. We provide a new relaxation of this notion of hitting time on a given subgraph and use that relaxation to construct a subgraph detection algorithm that can be computed easily and a generalization to K -partitioning schemes. Using a modification of the subgraph detector on each component, we propose a graph partitioner that identifies regions where random walks live for comparably large times. Importantly, our method implicitly respects the directed nature of the data for directed graphs while also being applicable to undirected graphs. We apply the partitioning method for community detection to a large class of models and real -world data sets.

It is critical to support healthy development of alcohol-related habits, particularly in contexts with heightened risk such as college campuses. Mindfulness-based strategies are frequently used in interventions to reduce substance use in clinical populations, but their utility as a preventative strategy among emerging adults is less clear. Combining multivariate neuroimaging, intervention, and experience sampling methodologies, we tested the degree to which mindful attention reduces alcohol cravings in the laboratory and consumption in daily life in a sample of college students. Students completed a mindful attention task towards alcohol in an fMRI scanner followed by a 28-day, smartphone-based, experience sampling intervention. We leveraged functional neuroimaging and machine learning to develop a neural measure (signature) of mindful attention that enabled us to examine moment-to-moment fluctuations and individual differences in effective implementation of mindful attention. In the laboratory, mindfully attending to alcohol decreased craving, particularly among people who more strongly expressed the mindful attention signature. In daily life, the mindful attention intervention increased mindful responses to alcohol and decreased lagged alcohol consumption through two distinct pathways: mindful responses directly influenced alcohol consumption and indirectly influenced it by reducing cravings for alcohol. Moreover, individuals who more strongly expressed the mindful attention signature benefitted the most from the intervention. Broadly, our study highlights how mindful attention can reduce alcohol consumption among emerging adults in college via a scalable smartphone-based intervention.

Genealogical networks (i.e. family trees) are of growing interest, with the largest known data sets now including well over one billion individuals. Interest in family history also supports an 8.5 billion dollar industry whose size is projected to double within 7 years (FutureWise report HC1137). Yet little mathematical attention has been paid to the complex network properties of genealogical networks, especially at large scales. The structure of genealogical networks is of particular interest due to the practice of forming unions, e.g. marriages, that are typically well outside one's immediate family. In most other networks, including other social networks, no equivalent restriction exists on the distance at which relationships form. To study the effect this has on genealogical networks we use persistent homology to identify and compare the structure of 101 genealogical and 31 other social networks. Specifically, we introduce the notion of a network's persistence curve, which encodes the network's set of persistence intervals. We find that the persistence curves of genealogical networks have a distinct structure when compared to other social networks. This difference in structure also extends to subnetworks of genealogical and social networks suggesting that, even with incomplete data, persistent homology can be used to meaningfully analyze genealogical networks. Here we also describe how concepts from genealogical networks, such as common ancestor cycles, are represented using persistent homology. We expect that persistent homology tools will become increasingly important in genealogical exploration as popular interest in ancestry research continues to expand.

Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.

Modifying behaviors, such as alcohol consumption, is difficult. Creating psychological distance between unhealthy triggers and one's present experience can encourage change. Using two multisite, randomized experiments, we examine whether theory-driven strategies to create psychological distance-mindfulness and perspective-taking-can change drinking behaviors among young adults without alcohol dependence via a 28-day smartphone intervention (Study 1, N = 108 participants, 5492 observations; Study 2, N = 218 participants, 9994 observations). Study 2 presents a close replication with a fully remote delivery during the COVID-19 pandemic. During weeks when they received twice-a-day intervention reminders, individuals in the distancing interventions reported drinking less frequently than on control weeks-directionally in Study 1, and significantly in Study 2. Intervention reminders reduced drinking frequency but did not impact amount. We find that smartphone-based mindfulness and perspective-taking interventions, aimed to create psychological distance, can change behavior. This approach requires repeated reminders, which can be delivered via smartphones.

Together, data from brain scanners and smartphones have sufficient coverage of biology, psychology, and environment to articulate between-person differences in the interplay within and across biological, psychological, and environmental systems thought to underlie psychopathology. An important next step is to develop frameworks that combine these two modalities in ways that leverage their coverage across layers of human experience to have maximum impact on our understanding and treatment of psychopathology. We review literature published in the last 3 years highlighting how scanners and smartphones have been combined to date, outline and discuss the strengths and weaknesses of existing approaches, and sketch a network science framework heretofore underrepresented in work combining scanners and smartphones that can push forward our understanding of health and disease.

This paper explores supply chain viability through empirical network-level analysis of supplier reachability under various scenarios. Specifically, this study investigates the effect of multi-tier random failures across different scales, as well as intelligent attacks on the global supply chain of medical equipment, an industry whose supply chain's viability was put under a crucial test during the COVID-19 pandemic. The global supply chain data was mined and analyzed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analyzed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimization tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern in the medical equipment sector. We also developed data-driven-tools to identify the thresholds for the breakdown and fragmentation of the medical equipment supply chain when faced with random failures, or different intelligent attack scenarios. The novel network analysis tools utilized in the study can be applied to the study of supply chain reachability and viability in other industries.