Open-World Semi-Supervised Learning for Node Classification
Yanling Wang Jing Zhang Lingxi Zhang Lixin Liu Yuxiao Dong Cuiping Li Hong Chen Hongzhi Yin
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.