Open-World Semi-Supervised Learning for Node Classification
<|reference_start|>Open-World Semi-Supervised Learning for Node Classification: 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 imbalance can negatively impact the model performance. Pre-trained feature encoders can alleviate this issue via producing compact representations for novel classes. However, creating general pre-trained encoders for various types of graph data has been proven to be challenging. As such, there is a demand for an effective method that does not rely on pre-trained graph encoders. In this paper, we propose an IMbalance-Aware method named OpenIMA for Open-world semi-supervised node classification, which trains the node classification model from scratch via contrastive learning with bias-reduced pseudo labels. Extensive experiments on seven popular graph benchmarks demonstrate the effectiveness of OpenIMA, and the source code has been available on GitHub.<|reference_end|>
@article{wang2024open-world,
    title={Open-World Semi-Supervised Learning for Node Classification},
    author={Yanling Wang, Jing Zhang, Lingxi Zhang, Lixin Liu, Yuxiao Dong,
  Cuiping Li, Hong Chen, Hongzhi Yin},
    journal={arXiv preprint arXiv:2403.11483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11483},
    primaryClass={cs.LG cs.AI cs.SI}
}