arXiv:2512.06154v1 Announce Type: new 
Abstract: Learning invariant graph representations for out-of-distribution (OOD) generalization remains challenging because the learned representations often retain spurious components. To address this challenge, this work introduces a new tool from information theory called Partial Information Decomposition (PID) that goes beyond classical information-theoretic measures. We identify limitations in existing approaches for invariant representation learning that solely rely on classical information-theoretic measures, motivating the need to precisely focus on redundant information about the target $Y$ shared between spurious subgraphs $G_s$ and invariant subgraphs $G_c$ obtained via PID. Next, we propose a new multi-level optimization framework that we call -- Redundancy-guided Invariant Graph learning (RIG) -- that maximizes redundant information while isolating spurious and causal subgraphs, enabling OOD generalization under diverse distribution shifts. Our approach relies on alternating between estimating a lower bound of redundant information (which itself requires an optimization) and maximizing it along with additional objectives. Experiments on both synthetic and real-world graph datasets demonstrate the generalization capabilities of our proposed RIG framework.

تقدم دراسة جديدة إطارًا يسمى التعلم البياني invariant-guided بواسطة المعلومات الزائدة (RIG)، والذي يستخدم تحليل المعلومات الجزئية (PID) لتحسين التعميم خارج التوزيع (OOD) في تعلم تمثيلات الرسوم البيانية. تهدف هذه الطريقة إلى التخفيف من احتفاظ التمثيلات المتعلمة بالمكونات الزائفة من خلال تعظيم المعلومات الزائدة مع عزل المجموعات السببية.

Un nuevo estudio presenta un marco denominado aprendizaje gráfico invariante guiado por la redundancia (RIG), que utiliza la descomposición de información parcial (PID) para mejorar la generalización fuera de distribución (OOD) en el aprendizaje de representaciones gráficas. Este enfoque busca mitigar la retención de componentes espurios en las representaciones aprendidas al maximizar la información redundante mientras aísla los subgrupos causales.

Une nouvelle étude présente un cadre appelé apprentissage graphique invariant guidé par la redondance (RIG), qui utilise la décomposition d'information partielle (PID) pour améliorer la généralisation hors distribution (OOD) dans l'apprentissage des représentations graphiques. Cette approche vise à atténuer la rétention de composants fallacieux dans les représentations apprises en maximisant l'information redondante tout en isolant les sous-graphes causaux.

A new study introduces a framework called Redundancy-guided Invariant Graph learning (RIG), which utilizes Partial Information Decomposition (PID) to enhance out-of-distribution (OOD) generalization in graph representation learning. This approach aims to mitigate the retention of spurious components in learned representations by maximizing redundant information while isolating causal subgraphs.

Learning Invariant Graph Representations Through Redundant Information

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