  In recent years, heterogeneous graph few-shot learning has been proposed to
address the label sparsity issue in heterogeneous graphs (HGs), which contain
various types of nodes and edges. The existing methods have achieved good
performance by transferring generalized knowledge extracted from rich-labeled
classes in source HG(s) to few-labeled classes in a target HG. However, these
methods only consider the single-heterogeneity scenario where the source and
target HGs share a fixed set of node/edge types, ignoring the more general
scenario of cross-heterogeneity, where each HG can have a different and
non-fixed set of node/edge types. To this end, we focus on the unexplored
cross-heterogeneity scenario and propose a novel model for Cross-heterogeneity
Graph Few-shot Learning, namely CGFL. In CGFL, we first extract meta-patterns
to capture heterogeneous information and propose a multi-view heterogeneous
graph neural network (MHGN) to learn meta-patterns across HGs. Then, we propose
a score module to measure the informativeness of labeled samples and determine
the transferability of each source HG. Finally, by integrating MHGN and the
score module into a meta-learning mechanism, CGFL can effectively transfer
generalized knowledge to predict new classes with few-labeled data. Extensive
experiments on four real-world datasets have demonstrated the superior
performance of CGFL over the state-of-the-art methods.
