A Review of Few-Shot and Zero-Shot Learning for Node Classification in Social Networks

Node classification tasks aim to assign labels or categories to entire graphs based on their structural properties or node attributes. It can be adopted for various types of graph systems, including but not limited to network traffic, biological networks, knowledge graphs, etc., especially to social networks. This problem is well-studied, and solutions have demonstrated significant success in numerous real-world applications. However, in the situation where emerging categories are scarce or even have no labeled data, classical methods perform poorly on the whole, which has attracted growing attention. Based on this, in this article, we divide researches for node classification in social networks into two broad categories: traditional methods and novel strategies (few-shot/zero-shot learning). In traditional node classification methods, we summarize some classical methods for both homogeneous and heterogeneous networks, which includes unsupervised classifier, matrix factorization techniques, supervised methods, random-walk, and meta-path. Meanwhile, we introduce novel methods in few-shot or zero-shot learning. The article outlines the technical principles of various methods and analyzes their performance across different classes. It further summarizes the benchmark datasets used for evaluating node classification tasks. Finally, the major opportunities, challenges, and future research directions in few-shot and zero-shot learning for node classification in graph scenarios are discussed.