EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network

• URL: http://arxiv.org/abs/2506.07288v3
• Date: Fri, 01 Aug 2025 08:06:28 GMT
• Title: EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network
• Authors: Weijie Guan, Haohui Wang, Jian Kang, Lihui Liu, Dawei Zhou,
• Abstract summary: This paper introduces Evidential Reasoning Network (EVINET), a framework that integrates Beta embedding within a subjective logic framework.<n>EVINET includes two key modules: Dissonance Reasoning for misclassification detection and Vacuity Reasoning for out-of-distribution detection.<n> Extensive experiments demonstrate that EVINET outperforms state-of-the-art methods across multiple metrics.
• Score: 13.114983443216511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph learning has been crucial to many real-world tasks, but they are often studied with a closed-world assumption, with all possible labels of data known a priori. To enable effective graph learning in an open and noisy environment, it is critical to inform the model users when the model makes a wrong prediction to in-distribution data of a known class, i.e., misclassification detection or when the model encounters out-of-distribution from novel classes, i.e., out-of-distribution detection. This paper introduces Evidential Reasoning Network (EVINET), a framework that addresses these two challenges by integrating Beta embedding within a subjective logic framework. EVINET includes two key modules: Dissonance Reasoning for misclassification detection and Vacuity Reasoning for out-of-distribution detection. Extensive experiments demonstrate that EVINET outperforms state-of-the-art methods across multiple metrics in the tasks of in-distribution classification, misclassification detection, and out-of-distribution detection. EVINET demonstrates the necessity of uncertainty estimation and logical reasoning for misclassification detection and out-of-distribution detection and paves the way for open-world graph learning. Our code and data are available at https://github.com/SSSKJ/EviNET.

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