  Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in
modeling real-world Heterogeneous Information Networks (HINs), challenges such
as expressiveness limitations and over-smoothing have prompted researchers to
explore Graph Transformers (GTs) for enhanced HIN representation learning.
However, research on GT in HINs remains limited, with two key shortcomings in
existing work: (1) A node's neighbors at different distances in HINs convey
diverse semantics. Unfortunately, existing methods ignore such differences and
uniformly treat neighbors within a given distance in a coarse manner, which
results in semantic confusion. (2) Nodes in HINs have various types, each with
unique semantics. Nevertheless, existing methods mix nodes of different types
during neighbor aggregation, hindering the capture of proper correlations
between nodes of diverse types. To bridge these gaps, we design an innovative
structure named (k,t)-ring neighborhood, where nodes are initially organized by
their distance, forming different non-overlapping k-ring neighborhoods for each
distance. Within each k-ring structure, nodes are further categorized into
different groups according to their types, thus emphasizing the heterogeneity
of both distances and types in HINs naturally. Based on this structure, we
propose a novel Hierarchical Heterogeneous Graph Transformer (HHGT) model,
which seamlessly integrates a Type-level Transformer for aggregating nodes of
different types within each k-ring neighborhood, followed by a Ring-level
Transformer for aggregating different k-ring neighborhoods in a hierarchical
manner. Extensive experiments are conducted on downstream tasks to verify
HHGT's superiority over 14 baselines, with a notable improvement of up to
24.75% in NMI and 29.25% in ARI for node clustering task on the ACM dataset
compared to the best baseline.
