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arxiv: 2605.25875 · v1 · pith:55FMDJIYnew · submitted 2026-05-25 · ⚛️ physics.soc-ph · physics.app-ph· q-bio.PE

The impact of behavioral homophily and conformity on epidemic spreading in networks with large groups

Olivier Ribordy , Clara Granell , Guillaume St-Onge , Laurent H\'ebert-Dufresne , Alex Arenas , Antoine Allard This is my paper

Pith reviewed 2026-06-29 19:24 UTC · model grok-4.3

classification ⚛️ physics.soc-ph physics.app-phq-bio.PE
keywords behavioral homophilyconformityepidemic spreadingclique networksSIS dynamicsapproximate master equationsgroup compositionminority behaviors
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The pith

Behavioral homophily amplifies conformity in large groups, letting minority behaviors persist and shifting epidemic thresholds on clique networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines the combined effects of behavioral homophily and conformity on SIS epidemic spreading in networks made of large cliques. Individuals hold intrinsic behavioral preferences but adjust their expressed behavior according to the makeup of their group. Through the approximate master equations framework, the analysis shows that homophily strengthens conformity effects inside big groups, which lets minority behaviors endure and alters both the epidemic threshold and the overall spreading patterns.

Core claim

In networks composed of large cliques, behavioral homophily amplifies the effects of conformity, modeled as the modulation of individual behavior by group composition, enabling minority behaviors to persist while substantially shifting epidemic thresholds and spreading regimes in the SIS process, as characterized by the approximate master equations framework.

What carries the argument

Approximate master equations framework on clique networks, with conformity implemented as behavior modulation according to group composition.

Load-bearing premise

The approximate master equations framework with conformity modeled as modulation by group composition accurately represents the interplay between behavioral heterogeneity and epidemic localization on clique networks.

What would settle it

Measuring whether epidemic thresholds and the persistence of minority behaviors in a real clique-like network match the shifts predicted by the model when homophily and group sizes are known.

Figures

Figures reproduced from arXiv: 2605.25875 by Alex Arenas, Antoine Allard, Clara Granell, Guillaume St-Onge, Laurent H\'ebert-Dufresne, Olivier Ribordy.

Figure 1
Figure 1. Figure 1: FIG. 1. Two representations of a small interaction network composed of 7 individuals (circles) and 4 groups. (a) In the bipartite [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Response probability of Eq [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Stationary state as a function of the unprotected transmission rate [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Impact of homophily on the contribution of each group type to the stationary state. Here, [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Normalized epidemic threshold as a function of homophily [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Value of the homophily paramater that maximizes [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Comparison of the model predictions with Monte-Carlo simulations with parameters [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original abstract

Understanding how social behavior influences epidemic dynamics has become a central focus in mathematical epidemiology. In particular, \textit{behavioral homophily} (the tendency of individuals to associate with similar others) and \textit{conformity} (the adjustment of individual behavior to group norms) are key mechanisms in shaping transmission patterns. In this work, we investigate the combined impact of these behavioral processes on the susceptible-infected-susceptible (SIS) dynamics on networks with large, densely connected groups, modeled as cliques. Each individual has an intrinsic behavioral preference, but their expressed behavior within a group is modulated by its composition, reflecting conformity dynamics. Using the approximate master equations (AME) framework, we characterize the interplay between behavioral heterogeneity, group structure, and epidemic localization. Our results reveal that behavioral homophily amplifies the effects of conformity in large groups, enabling minority behaviors to persist as well as substantially shifting epidemic thresholds and spreading regimes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper studies the combined effects of behavioral homophily and conformity on SIS epidemic dynamics in networks composed of large cliques. Individuals possess intrinsic behavioral preferences, but their expressed behavior is modulated by instantaneous group composition to model conformity. The approximate master equations (AME) framework is used to analyze how these mechanisms interact with behavioral heterogeneity and group structure, with the central claim that homophily amplifies conformity effects in large groups, enabling persistence of minority behaviors and substantially shifting epidemic thresholds and spreading regimes.

Significance. If the AME closure accurately captures the coupled behavioral-epidemic dynamics under composition-dependent modulation, the results would offer a mechanistic explanation for how social processes alter epidemic localization and thresholds in clique-structured networks. The work builds on standard AME techniques but extends them to state-dependent behavioral switching; however, the absence of closure validation or stochastic benchmarks limits the strength of the claimed threshold shifts.

major comments (2)
  1. [Methods/AME derivation] The central claims regarding threshold shifts and minority persistence rest on the accuracy of the AME moment closure under the conformity rule, which introduces rapid, composition-dependent behavioral switching inside cliques. Standard AME closures rely on assumptions of fixed or slowly varying node states and independence in the truncation; no quantification of closure error or direct comparison to exact stochastic simulations on the clique networks is provided to confirm that these assumptions hold.
  2. [Results] The abstract states that homophily 'amplifies the effects of conformity... substantially shifting epidemic thresholds,' but without reported benchmarks against individual-based simulations or alternative closures, it is unclear whether the observed regime changes are robust to the approximation or artifacts of the moment truncation.
minor comments (1)
  1. [Abstract] The abstract provides only high-level descriptions of results without equations or validation details, making it difficult to assess the derivations at first reading.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We agree that explicit validation of the AME closure against stochastic simulations is needed to strengthen the claims on threshold shifts and minority persistence, and we will incorporate such benchmarks in the revision.

read point-by-point responses

  1. Referee: [Methods/AME derivation] The central claims regarding threshold shifts and minority persistence rest on the accuracy of the AME moment closure under the conformity rule, which introduces rapid, composition-dependent behavioral switching inside cliques. Standard AME closures rely on assumptions of fixed or slowly varying node states and independence in the truncation; no quantification of closure error or direct comparison to exact stochastic simulations on the clique networks is provided to confirm that these assumptions hold.

    Authors: We acknowledge that the rapid behavioral switching induced by conformity challenges the standard AME assumptions of slowly varying states. Our derivation explicitly tracks the composition-dependent transition rates within the master equations for each behavioral and infection class, rather than assuming fixed states. To address the lack of validation, we will add a new subsection comparing AME predictions to individual-based stochastic simulations on finite clique networks (for clique sizes up to 50, where exact sampling is feasible), including quantitative error measures across homophily and conformity parameters. revision: yes

  2. Referee: [Results] The abstract states that homophily 'amplifies the effects of conformity... substantially shifting epidemic thresholds,' but without reported benchmarks against individual-based simulations or alternative closures, it is unclear whether the observed regime changes are robust to the approximation or artifacts of the moment truncation.

    Authors: The referee is correct that the abstract's claims on threshold shifts would be strengthened by direct benchmarks. We will revise the abstract to qualify the language and add simulation comparisons in the results section to confirm that the reported regime changes (including minority persistence and shifted thresholds) are reproduced by stochastic realizations and are not truncation artifacts. These comparisons will also test sensitivity to alternative moment closures where feasible. revision: yes

Circularity Check

0 steps flagged

No circularity: model-derived thresholds independent of fitted inputs

full rationale

The paper constructs an explicit dynamical model (SIS on cliques with state-dependent conformity modulated by instantaneous group composition) and applies the standard AME moment closure to obtain equations for the evolution of behavioral and infection states. Thresholds and regime shifts are obtained by analyzing the resulting closed system of ODEs; no parameters are fitted to epidemic data and then relabeled as predictions, no self-citation chain is invoked to justify uniqueness or closure accuracy, and the behavioral rules are stated directly rather than smuggled via prior work. The derivation chain is therefore self-contained and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on abstract; no explicit free parameters, axioms, or invented entities are detailed in the provided text.

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