Aug 2026· Chaos· Vol 36 8· 0 citations· 31 references
Medicine
TL;DR
A new descriptive homophily measure for general social hypergraphs where group interactions involve arbitrary number of individuals is proposed, and constraints of monotonic and majority homophily for two-class labels are established, providing a framework for analyzing homophily patterns.
Abstract
Homophily, the tendency of individuals to interact with similar others, is key to understand social dynamics. This concept has traditionally been measured in a k-uniform hypergraph model that accounts group interactions involving exactly k individuals (k ≥ 2). However, real-world interactions do not always involve the same number of individuals. Thus, in this paper, we propose a new descriptive homophily measure for general social hypergraphs where group interactions involve arbitrary number of individuals. We establish constraints of monotonic and majority homophily for two-class labels, providing a framework for analyzing homophily patterns. Experiments on several datasets reveal systematic deviations in hyperedge composition associated with node class labels relative to a label-independent baseline, offering insights into homophily pattern in complex social networks. This work bridges the gap between theoretical measures and practical applications in non-uniform hypergraphs, advancing the understanding of social and structural dynamics.
There are many extant models of hypergraphs with interactions governed by attribute-based homophily between nodes, but most assume independence between edges conditional on node parameters. Relaxing this assumption, we study a mechanistic model of growing hypergraphs in which edge formation is influenced by both previous edges and binary node labels. Edges form in this model as noisy copies of previous edges, where the transmission of nodes from one edge to the next depends on multiple homophilic mechanisms between labels. These homophilic mechanisms give rise to tunable assortative structure in the hypergraph. We derive a power law for the degree distribution in this model and describe the long-term dynamics of the joint distribution of labels contained in edges. Our model defines a likelihood over a labeled hypergraph, allowing us to use standard maximum-likelihood techniques to structure algorithms. We estimate the model parameters on synthetic and real data via (stochastic) expectation maximization. These estimates give statistically-principled descriptions of the operation of homophily in empirical polyadic systems. We also demonstrate an approach to community detection via simulated annealing which, though computationally expensive, achieves competitive results on both synthetic data and certain empirical data sets known to be challenging to community detection techniques based on the edge-independence assumption. Our findings highlight the benefits of incorporating edge- and label-dependence in higher-order modeling and data analysis, and point to several directions for future work.
Violet Ross, Francis Cataldo, Philip S. Chodrow· arXiv.org· 0 citations
Homophily is pervasive in online social media. While many empirical studies have relied on external sociodemographic traits to investigate it, significantly less is known about homophily at the cognitive level, that is, at the level of shared opinions or values. For such “value homophily”, we study interval-based patterns of opinion homophily from a bounded confidence perspective. We consider three heterogeneous datasets from Reddit and Twitter covering polarizing issues, with user opinions quantified via sentiment analysis and fact-checking, and analyze the interaction networks formed by weaker (reply-based) and stronger (follow-based) social ties. Our findings show that users’ interaction neighborhoods are significantly more concentrated in opinion space than expected by chance, with tie strength and issue polarization further amplifying this effect. Moreover, users often exhibit asymmetric tolerance ranges, with asymmetry typically directed toward locally mainstream positions rather than more radical or opposing ones. These findings support a bounded confidence interpretation of value homophily and provide a basis for future studies of exposure and polarization. Homophily in online social media is often examined through external traits, but cognitive-level homophily, based on shared opinions or values, remains underexplored. Here, the authors analyze opinion homophily using opinion estimates based on users’ posts and shared links on Reddit and Twitter, revealing concentrated interaction neighborhoods and asymmetric tolerance, highlighting implications for understanding exposure and polarization dynamics.
Yangyang Luan, C. Ancona, C. Bernardo et al.· Communications Physics· 0 citations
This work generalizes three distance-based topological measures, namely closeness centrality, betweenness centrality and node eccentricity, using this new hypergraph distance, and shows that hypergraphs can be divided into three distinct classes, corresponding to the possible dominance of specific orders of interaction over their general metric structure.
E. Vasilyeva, L. Tupikina, D. Musatov et al.· Chaos, Solitons & Fracta...· 0 citations
It is shown, for the Susceptible-Infectious threshold process on temporal higher-order networks derived from human face-to-face interactions, that the contribution of each hyperlink can be quantified by a contagion backbone, whose dependency on the diffusion parameters is demonstrated and supported by theoretical analysis.
Shilun Zhang, A. Ceria, Huijuan Wang· Communications Physics· 0 citations
This work proposes HAAM, an adaptive node classification approach for multiplex graphs that models per-dimension degrees of homophily and heterophily through dimension-specific compatibility matrices, and shows that HAAM achieves competitive performance compared to representative baseline methods.
K. Abdous, Nairouz Mrabah, M. Bouguessa· 0 citations
It is demonstrated that the information needed to classify heterophilic nodes correctly is often recoverable by retraining the classification head of a model, or even just the final linear classification layer.
Preben M. Ness, Fariz Ikhwantri, D. Marijan· 0 citations
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