Contrastive Disentangled Learning on Graph for Node Classification
Xiaojuan Zhang Jun Fu Shuang Li
Contrastive learning methods have attracted considerable attention due to
their remarkable success in analyzing graph-structured data. Inspired by the
success of contrastive learning, we propose a novel framework for contrastive
disentangled learning on graphs, employing a disentangled graph encoder and two
carefully crafted self-supervision signals. Specifically, we introduce a
disentangled graph encoder to enforce the framework to distinguish various
latent factors corresponding to underlying semantic information and learn the
disentangled node embeddings. Moreover, to overcome the heavy reliance on
labels, we design two self-supervision signals, namely node specificity and
channel independence, which capture informative knowledge without the need for
labeled data, thereby guiding the automatic disentanglement of nodes. Finally,
we perform node classification tasks on three citation networks by using the
disentangled node embeddings, and the relevant analysis is provided.
Experimental results validate the effectiveness of the proposed framework
compared with various baselines.