Weakly-Supervised Graph Classification with Even a Single Key Subgraph Per Class

• Published in: Proceedings (IEEE International Conference on Data Mining) pp. 947 - 952
• Main Authors: Zhang, Lu, Zhang, Chenbo, Guan, Jihong, Zhou, Shuigeng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.12.2024
• Subjects: Classification algorithms; Data mining; Drugs; graph classification; Iterative methods; Labeling; Minimization; Self-supervised learning; Social networking (online); subgraphs; Toxicology; Training; weakly-supervised learning
• ISSN: 2374-8486
• DOI: 10.1109/ICDM59182.2024.00120

Traditional graph classification requires large amounts of labeled data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for labeling graphs. Observing that some key subgraphs can determine the properties of graphs (e.g. the toxicity of drug molecules depend on some toxic functional groups), in this paper we explore to classify graphs using unlabeled graphs plus a small number of key subgraphs for each class, which is called weakly-supervised graph classification. To this end, we develop the WeGraph method, where the graph classifier is trained with subgraph-based self-supervised learning and divergence- minimization based fine-tuning. Moreover, we design a key subgraph extraction algorithm to iteratively extract and update the key subgraphs, which makes the training process a closed loop. We conduct extensive experiments on different types of graph datasets to evaluate the effectiveness of WeGraph. Experimental results show that WeGraph can achieve high performance even when only one key subgraph is provided for each class.