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.

OpenIMA 是一个不依赖预训练图编码器的鲁棒性、基于对比学习的方法，用于从头开始训练节点分类模型并通过减小偏差的伪标签来解决开放世界半监督节点分类问题。