Learning Invariant Graph Representations Through Redundant Information

• URL: http://arxiv.org/abs/2512.06154v1
• Date: Fri, 05 Dec 2025 21:07:11 GMT
• Title: Learning Invariant Graph Representations Through Redundant Information
• Authors: Barproda Halder, Pasan Dissanayake, Sanghamitra Dutta,
• Abstract summary: This work introduces a new tool from information theory called Partial Information Decomposition (PID)<n>We propose a new multi-level optimization framework that maximizes redundant information while isolating spurious subgraphs.<n> Experiments on both synthetic and real-world graph datasets demonstrate the generalization capabilities of our proposed RIG framework.
• Score: 10.145277735449831
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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