Not All Bonds Are Created Equal: Dyadic Latent Class Models for Relational Event Data

2025-01-08

AI Summary

Existing relational event models (REMs) typically model unobserved heterogeneity at the individual level, rendering them inadequate for capturing structural differences in dyadic interactions within dynamic social networks. To address this limitation, we propose a dyadic latent class REM that introduces latent classes at the dyad level—marking the first extension of latent variable modeling from the individual to the relational level—and thereby overcomes constraints imposed by adjacency matrix representations. Parameter estimation is conducted via maximum likelihood using the EM algorithm. Simulation studies demonstrate that our model achieves significantly higher goodness-of-fit and classification accuracy compared to conventional latent-variable REMs. When applied to cross-national military conflict data, it successfully identifies distinct types of country pairs exhibiting heterogeneous conflict propensities, uncovering previously obscured structural interaction patterns that standard models fail to detect.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Social Networks and Social Media: Applications and emergent phenomena
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

Dynamic social networks can be conceptualized as sequences of dyadic interactions between individuals over time. The relational event model has been the workhorse to analyze such interaction sequences in empirical social network research. When addressing possible unobserved heterogeneity in the interaction mechanisms, standard approaches, such as the stochastic block model, aim to cluster the variation at the actor level. Though useful, the implied latent structure of the adjacency matrix is restrictive which may lead to biased interpretations and insights. To address this shortcoming, we introduce a more flexible dyadic latent class relational event model (DLC-REM) that captures the unobserved heterogeneity at the dyadic level. Through numerical simulations, we provide a proof of concept demonstrating that this approach is more general than latent actor-level approaches. To illustrate the applicability of the model, we apply it to a dataset of militarized interstate conflicts between countries.

Problem

• Invisible Differences
• Relational Event Models
• Misinterpretation

Innovation

• Binary Latent Class Relational Event Model
• Invisible Heterogeneity in Dynamic Networks
• Superiority through Computer Simulation and Empirical Analysis