negMIX: Negative Mixup for OOD Generalization in Open-Set Node Classification

TL;DR

negMIX introduces a novel negative Mixup technique combined with cross-layer graph contrastive learning to improve open-set node classification, enhancing OOD generalization and class separation.

Contribution

The paper proposes negMIX, a new method that improves open-set node classification by enhancing OOD detection and class separation through negative Mixup and contrastive learning.

Findings

NegMIX outperforms state-of-the-art methods in various scenarios.

The negative Mixup improves OOD boundary clarity.

Contrastive learning enhances class compactness and separability.

Abstract

Open-set node classification (OSNC) allows unlabeled test data to contain novel classes previously unseen in the labeled data. The goal is to classify in-distribution (ID) nodes into corresponding known classes and reject out-of-distribution (OOD) nodes as unknown class. Despite recent notable progress in OSNC, two challenges remain less explored, i.e., how to enhance generalization to OOD nodes, and promote intra-class compactness and inter-class separability. To tackle such challenges, we propose a novel Negative Mixup with Cross-Layer Graph Contrastive Learning (negMIX) model. Firstly, we devise a novel negative Mixup method purposefully crafted for the open-set scenario with theoretical justification, to enhance the model's generalization to OOD nodes and yield clearer ID/OOD boundary. Additionally, a unique cross-layer graph contrastive learning module is developed to maximize the prototypical mutual information between the same class nodes across different topological distance neighborhoods, thereby facilitating intra-class compactness and inter-class separability. Extensive experiments validate significant outperformance of the proposed negMIX over state-of-the-art methods in various scenarios and settings.