Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in real-world scenarios, labeled graph data can be limited or scarce. To address this issue, we focus on the problem of semi-supervised graph classification, which involves both supervised and unsupervised models learning from labeled and unlabeled data. In contrast to recent approaches that transfer the entire knowledge from the unsupervised model to the supervised one, we argue that an effective transfer should only retain the relevant semantics that align well with the supervised task. In this paper, we propose a novel framework named DisenSemi, which learns disentangled representation for semi-supervised graph classification. Specifically, a disentangled graph encoder is proposed to generate factor-wise graph representations for both supervised and unsupervised models. Then we train two models via supervised objective and mutual information (MI)-based constraints respectively. To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task. Experimental results on a range of publicly accessible datasets reveal the effectiveness of our DisenSemi.

翻译：图分类是众多多媒体应用中的关键任务，其中图被用于表示各类多媒体数据，包括图像、视频和社交网络。然而，在现实场景中，带标签的图数据可能有限或稀缺。为解决这一问题，我们聚焦于半监督图分类问题，该问题涉及监督模型和无监督模型同时从带标签和无标签数据中学习。与近期将无监督模型的全部知识迁移至监督模型的方法不同，我们认为有效的迁移应仅保留与监督任务高度一致的相关语义。本文提出了一种名为DisenSemi的新框架，通过学习解耦表征来实现半监督图分类。具体而言，我们设计了一个解耦图编码器，为监督模型和无监督模型生成因子化的图表示。随后，我们分别通过监督目标函数和基于互信息的约束对两个模型进行训练。为确保从无监督编码器到监督编码器的知识迁移具有意义，我们进一步定义了两个模型间基于互信息的解耦一致性正则化，并识别出与当前图分类任务高度匹配的对应原理。在一系列公开可访问数据集上的实验结果验证了所提DisenSemi框架的有效性。