  Self-supervised learning (especially contrastive learning) methods on
heterogeneous graphs can effectively get rid of the dependence on supervisory
data. Meanwhile, most existing representation learning methods embed the
heterogeneous graphs into a single geometric space, either Euclidean or
hyperbolic. This kind of single geometric view is usually not enough to observe
the complete picture of heterogeneous graphs due to their rich semantics and
complex structures. Under these observations, this paper proposes a novel
self-supervised learning method, termed as Geometry Contrastive Learning (GCL),
to better represent the heterogeneous graphs when supervisory data is
unavailable. GCL views a heterogeneous graph from Euclidean and hyperbolic
perspective simultaneously, aiming to make a strong merger of the ability of
modeling rich semantics and complex structures, which is expected to bring in
more benefits for downstream tasks. GCL maximizes the mutual information
between two geometric views by contrasting representations at both local-local
and local-global semantic levels. Extensive experiments on four benchmarks data
sets show that the proposed approach outperforms the strong baselines,
including both unsupervised methods and supervised methods, on three tasks,
including node classification, node clustering and similarity search.
