arXiv subjects

Nicholas A. Christakis

Publications and source records attributed to Nicholas A. Christakis.

At least 19 recordsLinked to original sources

arXiv2026

Modeling Duelling Contagions of True and False Information in the Face of Inherent Individual biases

Advanced digital communication has revolutionized how people create and consume information, making information diffusion an important topic of research for domains from public health to national security. Real-world scenarios of information diffusion often involve competing narratives - true and false - spreading simultaneously. We propose a novel agent-based co-diffusion model, grounded in "complex-contagion" and "spiral of silence" theories, to capture how network dynamics exploit cognitive biases to shape such interactions. Our findings reveal that manipulative narratives dominate when early spreaders hold them. These network dynamics further exploit inherent cognitive biases to amplify information diffusion regardless of veracity. Further, while favourable previous experience strengthen collective optimism, unfavourable experiences attenuate optimism only modestly. However, we found that early seeding of agents with lower self-censorship not only constrains the spread of manipulation but can also lead to dominance of well-informed populance. This has implications for policies that aim to facilitate healthier discourse, strengthen social cohesion, and ensure equitable access to reliable information.

cs.SI↗

arXiv2026

Cultural Evolution of Perfumes since 1900

Perfumes are cultural artifacts and works of sensory art, composed from a finite, recombinable palette of notes that together evoke a distinctive scent impression. Here, we assemble the largest perfume corpus compiled to date, spanning multiple independent databases from 1900 to 2024, and study its evolution through a multidisciplinary computational framework. We first characterize perfumes by properties such as complexity and novelty using crowd-sourced data, finding that compositions have grown more minimalist in their scent profiles, yet more novel in their note combinations, a transition that began in the 1990s. Next, we construct copy-lineage networks and examine how notes are selected across successive time windows. We show that although imitation is pervasive, a growing share of notes drifts free of selection, and original creations retain a measurable quality premium, where they last longer, project further, and earn higher regard. Finally, we construct the collaboration network of master perfumers and show that a perfumer's creative style behaves as a social contagion, transmitted through collaboration and decaying with social distance. We discuss the principal forces shaping this evolution, including regulatory restrictions, cultural change, and the consolidation in the industry. Our findings position perfume as a culturally evolving system, akin to music and fashion, through which societies express and communicate hedonic sensory experiences.

physics.soc-ph↗

arXiv2026

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models

Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, community detection, and related tasks. Yet a basic design choice, the latent dimension, is still treated as a brittle hyperparameter, fixed before training and tuned by held-out performance. Learned factors are also identifiable only up to rotation and rescaling, so the nominal rank rarely coincides with the quantity that governs model behavior. We propose Spectral Prefix Extraction and Capacity-Targeted Representation Analysis (Spectra), which replaces rank as the unit of analysis with the spectrum of a learned positive semidefinite kernel, trace-normalized so that spectra are comparable across fits. The normalized eigenvalues form a distribution on the simplex, and their Shannon effective rank acts both as a summary of learned capacity and as a controllable training-time coordinate: a single scalar shapes this realized dimension during training, and bisection targets any desired value within the rank cap. To theoretically support that, we show local regularity and monotonicity of the realized-dimension profile. Across collaboration, social, biological, and infrastructure networks, Spectra traces performance--capacity frontiers that make the trade-off between predictive accuracy and realized dimension visible. It performs competitively with strong link-prediction baselines, yields aligned lower-capacity views of the same fitted model through spectral prefixes, and provides a principled handle on capacity in the overparameterized regime. Capacity thus becomes a property of the fitted model rather than a hyperparameter of the training.

cs.LG↗

arXiv2026

Benchmark for Assessing Olfactory Perception of Large Language Models

Here we introduce the Olfactory Perception (OP) benchmark, designed to assess the capability of large language models (LLMs) to reason about smell. The benchmark contains 1,010 questions across eight task categories spanning odor classification, odor primary descriptor identification, intensity and pleasantness judgments, multi-descriptor prediction, mixture similarity, olfactory receptor activation, and smell identification from real-world odor sources. Each question is presented in two prompt formats, compound names and isomeric SMILES, to evaluate the effect of molecular representations. Evaluating 21 model configurations across major model families, we find that compound-name prompts consistently outperform isomeric SMILES, with gains ranging from +2.4 to +18.9 percentage points (mean approx +7 points), suggesting current LLMs access olfactory knowledge primarily through lexical associations rather than structural molecular reasoning. The best-performing model reaches 64.4\% overall accuracy, which highlights both emerging capabilities and substantial remaining gaps in olfactory reasoning. We further evaluate a subset of the OP across 21 languages and find that aggregating predictions across languages improves olfactory prediction, with AUROC = 0.86 for the best performing language ensemble model. LLMs should be able to handle olfactory and not just visual or aural information.

cs.CL↗

arXiv2026

Deep description of static and dynamic network ties in Honduran villages

We examine static and dynamic social network structure in 176 villages within the Copan Department of Honduras across two data waves (2016, 2019), using detailed data on multiplex networks for 20,232 individuals enrolled in a longitudinal survey. These networks capture friendship, health advice, financial help, and adversarial relationships, allowing us to show how cooperation and conflict jointly shape social structure. Using node-level network measures derived from near-census sociocentric village networks, we leverage mixed-effects zero-inflated negative binomial models to assess the influence of individual attributes, such as gender, marital status, education, religion, and indigenous status, and of village characteristics, on the dynamics of social networks over time. We complement these node-level models with dyadic assortativity (odds-ratio-based homophily) and community-level measures to describe how sorting by key attributes differs across network types and between waves. Our results demonstrate significant assortativity based on gender and religion, particularly within health and financial networks. Across networks, gender and religion exhibit the most consistent assortative mixing. Additionally, community-level assortativity metrics indicate that educational and financial factors increasingly influence social ties over time. Our findings provide insights into how personal attributes and community dynamics interact to shape network formation and socio-economic relationships in rural settings over time.

stat.AP↗

arXiv2026

How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare

Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

cs.CY↗

arXiv2025

Multiplex Networks Provide Structural Pathways for Social Contagion in Rural Social Networks

Human social networks are inherently multiplex, comprising overlapping layers of relationships. Different layers may have distinct structural properties and interpersonal dynamics, but also may interact to form complex interdependent pathways for social contagion. This poses a fundamental problem in understanding behavioral diffusion and in devising effective network-based interventions. Here, we introduce a new conceptualization of how much each network layer contributes to critical contagion pathways and quantify it using a novel metric, network torque. We exploit data regarding sociocentric maps of 110 rural Honduran communities using a battery of 11 name generators and an experiment involving an exogenous intervention. Using a novel statistical framework, we assess the extent to which specific network layers alter global connectivity and support the spread of three experimentally introduced health practices. The results show that specific relationship types - such as close friendships - particularly enable non-overlapping diffusion pathways, amplifying behavioral change at the village level. For instance, non-redundant pathways enabled by closest friends can increase the adoption of correct knowledge about feeding newborns inappropriate chupones and enhance attitudes regarding fathers' involvement in postpartum care. Non-overlapping multiplex social ties are relevant to social contagion and social coherence in traditionally organized social systems.

cs.SI↗

arXiv2025

Modeling roles and trade-offs in multiplex networks

A multiplex social network captures multiple types of social relations among the same set of people, with each layer representing a distinct type of relationship. Understanding the structure of such systems allows us to identify how social exchanges may be driven by a person's own attributes and actions (independence), the status or resources of others (dependence), and mutual influence between entities (interdependence). Characterizing structure in multiplex networks is challenging, as the distinct layers can reflect different yet complementary roles, with interdependence emerging across multiple scales. Here, we introduce the Multiplex Latent Trade-off Model (MLT), a framework for extracting roles in multiplex social networks that accounts for independence, dependence, and interdependence. MLT defines roles as trade-offs, requiring each node to distribute its source and target roles across layers while simultaneously distributing community memberships within hierarchical, multi-scale structures. Applying the MLT approach to 176 real-world multiplex networks, composed of social, health, and economic layers, from villages in western Honduras, we see core social exchange principles emerging, while also revealing local, layer-specific, and multi-scale communities. Link prediction analyses reveal that modeling interdependence yields the greatest performance gains in the social layer, with subtler effects in health and economic layers. This suggests that social ties are structurally embedded, whereas health and economic ties are primarily shaped by individual status and behavioral engagement. Our findings offer new insights into the structure of human social systems.

cs.SI↗

arXiv2025

Countering the Forgetting of Novel Health Information with 'Social Boosting'

To mitigate the adverse effects of low-quality or false information, studies have shown the effectiveness of various intervention techniques through debunking or so-called pre-bunking. However, the effectiveness of such interventions can decay. Here, we investigate the role of the detailed social structure of the local villages within which the intervened individuals live, which provides opportunities for the targeted individuals to discuss and internalize new knowledge. We evaluated this with respect to a critically important topic, information about maternal and child health care, delivered via a 22-month in-home intervention. Specifically, we examined the effect of having friendship ties on the retention of knowledge interventions among targeted individuals in 110 isolated Honduran villages. We hypothesize that individuals who receive specific knowledge can internalize and consolidate this information by engaging in social interactions where, for instance, they have an opportunity to discuss it with others in the process. The opportunity to explain information to others (knowledge sharing) promotes deeper cognitive processing and elaborative encoding, which ultimately enhances memory retention. We found that well-connected individuals within a social network experience an enhanced effectiveness of knowledge interventions. These individuals may be more likely to internalize and retain the information and reinforce it in others, due to increased opportunities for social interaction where they teach others or learn from them, a mechanism we refer to as "social boosting". These findings underscore the role of social interactions in reinforcing health knowledge interventions over the long term. We believe these findings would be of interest to the health policy, the global health workforce, and healthcare professionals focusing on disadvantaged populations and UN missions on infodemics.

cs.SI↗

arXiv2025

Educational Intervention Re-Wires Social Interactions in Isolated Village Networks

Social networks shape behavior, disseminate information, and undergird collective action within communities. Consequently, they can be very valuable in the design of effective interventions to improve community well-being. But any exogenous intervention in networked groups, including ones that just involve the provision of information, can also possibly modify the underlying network structure itself, and some interventions are indeed designed to do so. While social networks obey certain fundamental principles (captured by network-level statistics, such as the degree distribution or transitivity level), they can nevertheless undergo change across time, as people form and break ties with each other within an overall population. Here, using a randomized controlled trial in 110 remote Honduran villages involving 8,331 people, we evaluated the effects of a 22-month public health intervention on pre-existing social network structures. We leverage a two-stage randomized design, where a varying fraction of households received the intervention in each village. In low-dosage villages (5%, 10%, 20%, and 30%) compared to untreated villages (0%), over a two-year period, individuals who received the intervention tended to sever both inbound and outbound ties with untreated individuals whom they previously trusted for health advice. Conversely, in high-dosage villages (50%, 75%, and 100%), treated individuals increased both their inbound and outbound ties. Furthermore, although the intervention was health-focused, it also reshaped broader friendship and financial ties. In aggregate, the imposition of a novel health information regime in rural villages (as a kind of social institution) led to a significant rewiring in individuals particular connections, but it still had a limited effect on the overall global structure of the village-wide social networks.

stat.AP↗

arXiv2024

Bringing Leaders of Network Sub-Groups Closer Together Does Not Facilitate Consensus

Consensus formation is a complex process, particularly in networked groups. When individuals are incentivized to dig in and refuse to compromise, leaders may be essential to guiding the group to consensus. Specifically, the relative geodesic position of leaders (which we use as a proxy for ease of communication between leaders) could be important for reaching consensus. Additionally, groups searching for consensus can be confounded by noisy signals in which individuals are given false information about the actions of their fellow group members. We tested the effects of the geodesic distance between leaders (geodesic distance ranging from 1-4) and of noise (noise levels at 0%, 5%, and 10%) by recruiting participants (N=3,456) for a set of experiments (n=216 groups). We find that noise makes groups less likely to reach consensus, and the groups that do reach consensus take longer to find it. We find that leadership changes the behavior of both leaders and followers in important ways (for instance, being labeled a leader makes people more likely to 'go with the flow'). However, we find no evidence that the distance between leaders is a significant factor in the probability of reaching consensus. While other network properties of leaders undoubtedly impact consensus formation, the distance between leaders in network sub-groups appears not to matter.

physics.soc-ph↗

arXiv2023

Antidepressant use and spatial social capital

Social capital may help individuals maintain their mental health. Most empirical work based on small-scale surveys finds that bonding social capital and cohesive social networks are critical for mental well-being, while bridging social capital and diverse networks are considered less important. Here, we link data on antidepressant use of 277,344 small-town residents to a nation-wide online social network. The data enable us to examine how individuals' mental healthcare is related to the spatial characteristics of their social networks including their strong and weak ties. We find that, besides the cohesion of social networks around home, the diversity of connections to distant places is negatively correlated with the probability of antidepressant use. Spatial diversity of social networks is also associated with decreasing dosage in subsequent years. This relationship is independent from the local access to antidepressants and is more prevalent for young individuals. Structural features of spatial social networks are prospectively associated with depression treatment.

physics.soc-ph↗

arXiv2023

It Is Easy For Multi-Issue Bundles To Advance Anti-Democratic Agendas

When confronted with a host of issues, groups often save time and energy by compiling many issues into a single bundle when making decisions. This reduces the time and cost of group decision-making, but it also leads to suboptimal outcomes as individuals lose the ability to express their preferences on each specific issue. We examine this trade-off by quantifying the value of bundled voting compared to a more tedious issue-by-issue voting process. Our research investigates multi-issue bundles and their division into multiple subbundles, confirming that bundling generally yields positive outcomes for the group. However, bundling and issue-by-issue voting can easily yield opposite results regardless of the number of votes the bundle receives. Furthermore, we show that most combinations of voters and issues are vulnerable to manipulation if the subundling is controlled by a bad actor. By carefully crafting bundles, such an antagonist can achieve the minority preference on almost every issue. Thus, naturally occurring undemocratic outcomes may be rare, but they can be easily manufactured. To thoroughly investigate this problem, we employ three techniques throughout the paper: mathematical analysis, computer simulations, and the analysis of American voter survey data. This study provides valuable insights into the dynamics of bundled voting and its implications for group decision-making. By highlighting the potential for manipulation and suboptimal outcomes, our findings add another layer to our understanding of voting paradoxes and offer insights for those designing group decision-making systems that are safer and more fair.

physics.soc-ph↗

arXiv2023

Mass Gatherings for Political Expression Had No Discernable Association with the Local Course of the COVID-19 Pandemic in the USA in 2020 and 2021

Epidemic disease can spread during mass gatherings. We assessed the impact on the local-area trajectory of the COVID-19 epidemic of a type of mass gathering about which comprehensive data were available. Here, we examined five types of political events in 2020 and 2021: the US primary elections; the US Senate special election in Georgia; the gubernatorial elections in New Jersey and Virginia; Donald Trump's political rallies; and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,119 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths, or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically insignificant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162), and 8.275 cases (95% CI = -1.383, 20.7), on each day, for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases, or transmissibility after mass gatherings for political expression during the first two years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.

stat.AP↗

arXiv2023

The Enmity Paradox

The "friendship paradox" of social networks states that, on average, "your friends have more friends than you do." Here, we theoretically and empirically explore a related and overlooked paradox we refer to as the "enmity paradox." We use empirical data from 24,687 people living in 176 villages in rural Honduras. We show that, for a real negative undirected network (created by symmetrizing antagonistic interactions), the paradox exists as it does in the positive world. Specifically, a person's enemies have more enemies, on average, than a person does. Furthermore, in a mixed world of positive and negative ties, we study the conditions for the existence of the paradox, both theoretically and empirically, finding that, for instance, a person's friends typically have more enemies than a person does. We also confirm the "generalized" enmity paradox for nontopological attributes in real data, analogous to the generalized friendship paradox (e.g., the claim that a person's enemies are richer, on average, than a person is). As a consequence, the naturally occurring variance in the degree distribution of both friendship and antagonism in social networks can skew people's perceptions of the social world.

cs.SI↗

arXiv2020

Testing for Balance in Social Networks

Friendship and antipathy exist in concert with one another in real social networks. Despite the role they play in social interactions, antagonistic ties are poorly understood and infrequently measured. One important theory of negative ties that has received relatively little empirical evaluation is balance theory, the codification of the adage `the enemy of my enemy is my friend' and similar sayings. Unbalanced triangles are those with an odd number of negative ties, and the theory posits that such triangles are rare. To test for balance, previous works have utilized a permutation test on the edge signs. The flaw in this method, however, is that it assumes that negative and positive edges are interchangeable. In reality, they could not be more different. Here, we propose a novel test of balance that accounts for this discrepancy and show that our test is more accurate at detecting balance. Along the way, we prove asymptotic normality of the test statistic under our null model, which is of independent interest. Our case study is a novel dataset of signed networks we collected from 32 isolated, rural villages in Honduras. Contrary to previous results, we find that there is only marginal evidence for balance in social tie formation in this setting.

stat.ME↗

arXiv2015

Spread of pathogens in the patient transfer network of US hospitals

Emergent antibiotic-resistant bacterial infections are an increasingly significant source of morbidity and mortality. Antibiotic-resistant organisms have a natural reservoir in hospitals, and recent estimates suggest that almost 2 million people develop hospital-acquired infections each year in the US alone. We investigate a network induced by the transfer of Medicare patients across US hospitals over a 2-year period to learn about the possible role of hospital-to-hospital transfers of patients in the spread of infections. We analyze temporal, geographical, and topological properties of the transfer network and demonstrate, using C. Diff. as a case study, that this network may serve as a substrate for the spread of infections. Finally, we study different strategies for the early detection of incipient epidemics, finding that using approximately 2% of hospitals as sensors, chosen based on their network in-degree, results in optimal performance for this early warning system, enabling the early detection of 80% of the C. Diff. cases.

physics.soc-ph↗

arXiv2014

Friendship and Natural Selection

More than any other species, humans form social ties to individuals who are neither kin nor mates, and these ties tend to be with similar people. Here, we show that this similarity extends to genotypes. Across the whole genome, friends' genotypes at the SNP level tend to be positively correlated (homophilic); however, certain genotypes are negatively correlated (heterophilic). A focused gene set analysis suggests that some of the overall correlation can be explained by specific systems; for example, an olfactory gene set is homophilic and an immune system gene set is heterophilic. Finally, homophilic genotypes exhibit significantly higher measures of positive selection, suggesting that, on average, they may yield a synergistic fitness advantage that has been helping to drive recent human evolution.

q-bio.GN↗