Open-World Semi-Supervised Learning for Node Classification

arXiv:2403.11483v1 Announce Type: new
Abstract: Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community. As only seen classes have human labels, they are usually better learned than novel classes, and thus exhibit smaller intra-class variances within the embedding space (named as imbalance of intra-class variances between seen and novel classes). Based on empirical and theoretical analysis, we find the variance …

abstract arxiv class classification community cs.ai cs.lg cs.si graph human labels multiple node nodes novel open-world practical semi-supervised semi-supervised learning ssl supervised learning type world