提出DIB-OD框架，提升异构图迁移的鲁棒性。
DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation
- 分离信息瓶颈与在线蒸馏，解耦可迁移核心与补充信息
- 跨类型迁移下7个数据集性能均优于基线，尤其在复杂场景
- 适合需要稳定跨领域图学习的应用场景
图预训练可促进图数据集间知识迁移，但结构与特征分布的显著差异可能导致负迁移及可复用知识被覆盖。本文提出DIB-OD框架，结合解耦信息瓶颈与在线蒸馏。多视图教师首先学习压缩且任务相关的表示，将其蒸馏至可迁移的核心分支与互补残差分支。通过互信息目标与基于HSIC的依赖正则化，抑制两分支间信息重叠；同时，置信度感知语义正则化与冻结教师确保目标域适应中可靠预训练信息不被破坏。在涵盖化学、生物与社交网络领域的7个图分类数据集上，实验表明其性能持续优于代表性基线，尤其在挑战性的跨类型迁移中表现突出。
原文摘要 · Abstract (English)
Graph pre-training can facilitate knowledge transfer across graph datasets, but severe structural and feature shifts may cause negative transfer and adaptation-induced overwriting of reusable knowledge. We propose DIB-OD, a heterogeneous graph adaptation framework that combines a Decoupled Information Bottleneck with Online Distillation. A multiview teacher first learns a compressed, task-relevant representation, which is distilled into a transferable core branch and a complementary residual branch. Mutual-information objectives and HSIC-based dependence regularization discourage information overlap between the branches, while a confidence-aware semantic regularizer and a frozen teacher anchor reliable pretrained information during target-domain adaptation. Experiments on seven graph-classification datasets spanning chemical, biological, and social-network domains show consistent improvements over representative baselines, particularly under challenging cross-type transfer.
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