  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.
