DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation

• URL: http://arxiv.org/abs/2604.10882v1
• Date: Mon, 13 Apr 2026 01:03:44 GMT
• Title: DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation
• Abstract summary: DIB-OD is a novel framework designed to preserve the invariant core for robust heterogeneous graph adaptation.<n>We show that DIB-OD significantly outperforms state-of-the-art methods, particularly in challenging inter-type domain transfers.
• Score: 9.626014518332898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Network pretraining is pivotal for leveraging unlabeled graph data. However, generalizing across heterogeneous domains remains a major challenge due to severe distribution shifts. Existing methods primarily focus on intra-domain patterns, failing to disentangle task-relevant invariant knowledge from domain-specific redundant noise, leading to negative transfer and catastrophic forgetting. To this end, we propose DIB-OD, a novel framework designed to preserve the invariant core for robust heterogeneous graph adaptation through a Decoupled Information Bottleneck and Online Distillation framework. Our core innovation is the explicit decomposition of representations into orthogonal invariant and redundant subspaces. By utilizing an Information Bottleneck teacher-student distillation mechanism and the Hilbert-Schmidt Independence Criterion, we isolate a stable invariant core that transcends domain boundaries. Furthermore, a self-adaptive semantic regularizer is introduced to protect this core from corruption during target-domain adaptation by dynamically gating label influence based on predictive confidence. Extensive experiments across chemical, biological, and social network domains demonstrate that DIB-OD significantly outperforms state-of-the-art methods, particularly in challenging inter-type domain transfers, showcasing superior generalization and anti-forgetting performance.

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