Explainability defines how people interpret and trust machine learning systems, making it central to the interaction between humans and algorithms. This study introduces an axiomatic benchmark to evaluate whether graph-based explainers faithfully reflect the decision process of the models they interpret. By systematically testing white-box classifiers and real-world networks, it exposes the conditions under which explainers deviate from model logic, offering a rigorous framework for assessing faithfulness and robustness in graph explainability.

Moderation policies aim to reduce harm online, yet banning conspiratorial communities can trigger migration and reformation elsewhere. Comparing users of banned Reddit groups with their counterparts on Voat, this study shows that conspiracy networks reconstruct their social ties and activity levels after deplatforming, sustaining both engagement and toxicity. The results underscore the adaptive resilience of conspiratorial ecosystems, calling for moderation strategies that account for cross-platform behavior.

Agent-based models explain how micro-level rules produce macro-level patterns, yet linking them to data remains a challenge. This paper presents a protocol for inferring latent agent variables through probabilistic modeling and gradient-based expectation maximization, demonstrated on a housing market simulation. The method enhances predictive accuracy and interpretability, promoting a data-driven way to calibrate generative models of collective behavior.

Online debates are often portrayed as ideological echo chambers, yet the structure of conversation can mirror offline social boundaries instead. Analyzing millions of interactions in Reddit’s news discussions, this study shows that users connect more across ideological lines than across demographic divides such as age and income. The findings reveal how latent social stratification shapes digital communication, suggesting that online polarization often reproduces material divisions rather than belief systems.