Node Classification With Reject Option

Uday Bhaskar Kuchipudi, Jayadratha Gayen, Charu Sharma, Naresh Manwani

Abstract: One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option settings has not been previously explored. In this paper, we propose NCwR, a novel approach to node classification in Graph Neural Networks (GNNs) with an integrated reject option. This allows the model to abstain from making predictions for samples with high uncertainty. We propose cost-based and coverage-based methods for classification with abstention in node classification settings using GNNs. We perform experiments using our method on standard citation network datasets Cora, CiteSeer, PubMed and ogbn-arxiv. We also model the Legal judgment prediction problem on the ILDC dataset as a node classification problem, where nodes represent legal cases and edges represent citations. We further interpret the model by analyzing the cases in which it abstains from predicting and visualizing which part of the input features influenced this decision.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: The previous submission got desk-rejected due to formatting issues (specifically wrong font style). We were advised to review and resubmit. We corrected the format to adhere with TMLR guidelines and resubmitting.

Assigned Action Editor: ~Sungsoo_Ahn1

Submission Number: 3854