EVINET: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments

dc.contributor.authorGuan, Weijieen
dc.contributor.authorWang, Haohuien
dc.contributor.authorKang, Jianen
dc.contributor.authorLiu, Lihuien
dc.contributor.authorZhou, Daweien
dc.date.accessioned2025-09-10T12:23:51Zen
dc.date.available2025-09-10T12:23:51Zen
dc.date.issued2025-08-03en
dc.date.updated2025-09-01T07:47:51Zen
dc.description.abstractGraph 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-ofdistribution 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 outof- 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3711896.3736945en
dc.identifier.urihttps://hdl.handle.net/10919/137728en
dc.language.isoenen
dc.publisherACMen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderThe author(s)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleE<small>VI</small>N<small>ET</small>: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environmentsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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