  Graph self-supervised learning (SSL) holds considerable promise for mining
and learning with graph-structured data. Yet, a significant challenge in graph
SSL lies in the feature discrepancy among graphs across different domains. In
this work, we aim to pretrain one graph neural network (GNN) on a varied
collection of graphs endowed with rich node features and subsequently apply the
pretrained GNN to unseen graphs. We present a general GraphAlign method that
can be seamlessly integrated into the existing graph SSL framework. To align
feature distributions across disparate graphs, GraphAlign designs alignment
strategies of feature encoding, normalization, alongside a
mixture-of-feature-expert module. Extensive experiments show that GraphAlign
empowers existing graph SSL frameworks to pretrain a unified and powerful GNN
across multiple graphs, showcasing performance superiority on both in-domain
and out-of-domain graphs.
