  Many real-world graphs frequently present challenges for graph learning due
to the presence of both heterophily and heterogeneity. However, existing
benchmarks for graph learning often focus on heterogeneous graphs with
homophily or homogeneous graphs with heterophily, leaving a gap in
understanding how methods perform on graphs that are both heterogeneous and
heterophilic. To bridge this gap, we introduce H2GB, a novel graph benchmark
that brings together the complexities of both the heterophily and heterogeneity
properties of graphs. Our benchmark encompasses 9 diverse real-world datasets
across 5 domains, 28 baseline model implementations, and 26 benchmark results.
In addition, we present a modular graph transformer framework UnifiedGT and a
new model variant, H2G-former, that excels at this challenging benchmark. By
integrating masked label embeddings, cross-type heterogeneous attention, and
type-specific FFNs, H2G-former effectively tackles graph heterophily and
heterogeneity. Extensive experiments across 26 baselines on H2GB reveal
inadequacies of current models on heterogeneous heterophilic graph learning,
and demonstrate the superiority of our H2G-former over existing solutions. Both
the benchmark and the framework are available on GitHub
(https://github.com/junhongmit/H2GB) and PyPI (https://pypi.org/project/H2GB),
and documentation can be found at https://junhongmit.github.io/H2GB/.
