Conference Agenda

Transitivity is important for diffusion processes on networks because a propensity for transitive closure leads to network clustering. Human social networks are characterized by high levels of transitive triangles relative to random graphs of the same density. However, measuring transitivity is difficult when collecting field data using egocentric designs. Homophily is a ubiquitous social process in human social relations and is easily measured with egocentric sampling. Homophily and transitivity are typically presented as potential confounds—that some particular network behavior may be caused by either homophily or transitivity. There is surprisingly little work describing the relationship between the two.

Here we ask whether a preference for homophilous ties can directly cause transitivity in social networks in the absence of a psychological preference for structural balance. To evaluate this, we used exponential random graph models to simulate an ensemble of group-structured networks with varying probabilities that an edge will connect members of the same group, and test whether transitivity emerges endogenously from only this preference for homophily. We find that indeed homophily can be sufficient to generate substantial transitivity in our simulated graphs. However, we also find that the degree to which homophily causes transitivity depends on network density: homophily is more likely to generate transitivity in higher-density networks, but has little-to-no effect on transitivity in lower-density networks. Thus, the effect of homophily on endogenous transitivity requires sufficient in-group connectivity so that new in-group ties have a relatively high probability of closing triangles.

This work has strong implications for understanding diffusion processes on networks such as the transmission dynamics of infectious disease. We apply these insights to simulating landscape-scale retrovirus transmission using an egocentric network data from western Uganda.

The minimal dominating set (MDS) is a well-established concept in network controllability and has been successfully applied in various domains, including sensor placement, network resilience, and epidemic containment. In this study, we adapt the local-improvement MDS routine and explore its potential for enhancing seed selection for influence maximisation in multilayer networks. We employ the Linear Threshold Model (LTM), which offers an intuitive representation of influence spread or opinion dynamics by accounting for peer influence accumulation. To ensure interpretability, we utilise rank-refining seed selection methods, with the results further filtered with MDS. Our findings reveal that incorporating MDS into the seed selection process improves spread only within a specific range of situations. Notably, the improvement is observed for larger seed set budgets, lower activation thresholds, and when an ”AND” strategy is used to aggregate influence across network layers. This scenario reflects situations where an individual does not require the majority of their acquaintances to hold a target opinion, but must be influenced across all social circles.

A preprint of the paper can be found here: https://arxiv.org/abs/2502.15236

This article advances the understanding of burnout contagion by proposing that burnout can spread across formal organizational groups, not just within them. Drawing on Job Demand-Resources and network theories, we argue that burnout propagates via intergroup work interactions, through changes in job demands, resources, and emotional contagion. Using data from 1,881 employees and 8 million e-mail exchanges in a South American university, we examine how burnout originating from intergroup collaborators predicts individual burnout. Our findings reveal that intergroup burnout contagion explains unique variance in individual-level exhaustion and disengagement beyond group-level contagion, with intergroup relationships accounting for up to 19% of variance in disengagement compared to 14% for within groups. Mediation analyses highlight the distinct pathways of job demands and emotional contagion in transmitting burnout across groups, but not job resources. These results underscore the importance of examining burnout as a networked phenomenon that extends beyond formal group boundaries, offering insights into how contemporary workplaces can address burnout in interconnected and cross-functional environments.

This study introduces a computational model integrating the homophic principle both as a structural efect (network clustering by sociodemographic similarity) and as a confirmation bias (preferential influence from socially proximate peers) to explain the bimodal success rates observed in innovation adoption. Building on critiques of structural reductionism in classical diffusion models (Goldberg, 2021), we propose that preference heterogeneity—not just network topology—drives phase transitions between adoption failure and success. Using ecological networks constructed from the American Trends Panel (ATP) dataset, we simulate diffusion dynamics where agents adopt innovations based on:

1. Utility thresholds (q_i), representing individual resistance levels, and

2. Minimum social proximity (h), modulating peer influence validity.

3. Agents adopt if an innovation’s intrinsic utility (Gamma) exceeds q_i, or if social reinforcement from peers within distance h surpasses adoption thresholds (tau_i). In this sense, social closeness activates threshold contagion.

Our framework challenges assumptions in prior works (Centola & Macy, 2007; Tur et al., 2024) by replacing synthetic networks with empirically grounded configurations (McPherson & Smith, 2019). Simulations on synthetic networks like Watts-Strogatz, scale-free, and Small-World SDA, show the emergence of phase transitions at critical values of h, demonstrating how homophily amplifies confirmation bias to create non-structural diffusion barriers. For example, at h = 0.2 (moderate social proximity), adoption rates shift abruptly from 18% to 92% as Gamma crosses a critical threshold, mirroring real-world “tipping point” dynamics (Granovetter, 1978).

Methodologically, we advance network science by:

- Estimating homophily parameters (beta_m) from ATP sociodemographics (McPherson & Smith, 2019),

- Creating real-world network topology based on survey data, and imputing preferences based on the individual characteristics of the nodes, and

- Validating through 1,200+ computational experiments on CHPC clusters.

Results of network topology align with empirical observations of cultural differentiation (DellaPosta et al., 2015) and rationalize bimodal market outcomes in technology and public health (Farrell, 1998). Crucially, we show that preference-driven homophily—not preexisting structural segregation—suffices to generate adoption bottlenecks, addressing critiques of earlier models (Goldberg, 2021).

This work bridges micro-level decision-making (rational choice theory) with macro-level diffusion patterns. By incorporating empirical social distances and preference distributions, we provide a paradigm shift from purely structural explanations to ecologically valid models of innovation spread.

- Goldberg, A. (2021). Associative Diffusion and the Pitfalls of Structural Reductionism. American Sociological Review, 86(6):1205–1210.

- Centola, D. and Macy, M. (2007). Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3):702–734.

- Tur, E. M., Zeppini, P., and Frenken, K. (2024). Diffusion in small worlds with homophily and social reinforcement: A theoretical model. Social Networks, 76:12–21.

- McPherson, M. and Smith, J. A. (2019). Network Effects in Blau Space: Imputing Social Context from Survey Data. Socius: Sociological Research for a Dynamic World, 5:2378023119868591.

- Granovetter, M. (1978). Threshold Models of Collective Behavior. American Journal of Sociology, 83(6):1420–1443.

- DellaPosta, D., Shi, Y., and Macy, M. (2015). Why Do Liberals Drink Lattes? American Journal of Sociology, 120(5):1473–1511.

- Farrell, W. (1998). How hits happen: forecasting predictability in a chaotic marketplace. Harper-Business, New York, 1st ed edition.

Theories of network diffusion often assume that contagions spread symmetrically through undirected ties. However, we show that complex contagions—where adoption requires reinforcement from multiple peers—exhibit emergent directedness, even in undirected networks. Using a novel causal modeling framework, we introduce Causal Tie Importance and Causal Flow Asymmetry to quantify influence propagation. Our analyses reveal that long ties often function as one-way conduits of influence, creating diffusion inequalities. Moreover, as reinforcement thresholds increase, contagion dynamics exhibit a core-periphery inversion, where diffusion is increasingly channeled from the periphery to the core, contradicting conventional assumptions about centrality and influence.

These findings challenge Granovetter’s "strength of weak ties" hypothesis and have direct implications for social capital and innovation diffusion. Applying our framework to empirical networks, we show that the influence of weak ties follows an inverse U-shaped pattern, as seen in a recent large-scale LinkedIn study on job diffusion. Rather than the weakest ties being most effective, moderately strong ties play the largest role in spreading job opportunities, while both very weak and very strong ties are less effective. Our results explain this pattern by showing that medium-strength ties optimally balance bridge formation and reinforcement.

Additionally, we analyze endogenous bridge formation in complex networks, showing that asymmetric bridges form naturally, while strategic interventions (e.g., triadic closure) are needed for integrative, bidirectional influence. These findings offer new insights into diffusion dynamics in complex networks, organizational structures, and policy systems.