  We propose a heterogeneous graph mamba network (HGMN) as the first
exploration in leveraging the selective state space models (SSSMs) for
heterogeneous graph learning. Compared with the literature, our HGMN overcomes
two major challenges: (i) capturing long-range dependencies among heterogeneous
nodes and (ii) adapting SSSMs to heterogeneous graph data. Our key contribution
is a general graph architecture that can solve heterogeneous nodes in
real-world scenarios, followed an efficient flow. Methodologically, we
introduce a two-level efficient tokenization approach that first captures
long-range dependencies within identical node types, and subsequently across
all node types. Empirically, we conduct comparisons between our framework and
19 state-of-the-art methods on the heterogeneous benchmarks. The extensive
comparisons demonstrate that our framework outperforms other methods in both
the accuracy and efficiency dimensions.
