Identify three key aspects that require focus when learning HGNNs representations based on meta-paths:

With the rapid advancement of deep learning, graph neural networks (GNNs) have achieved remarkable success in graph representation learning[1].

However, most GNN models assume the input graph is a homogeneous graph with uniform semantic types, while real-world graphs are often Heterogeneous Graph Neural Networks (HGNNs) rich in complex semantics. To effectively learn the feature representations of HGNNs, most existing studies rely on metapaths.

A meta-path is a sequence of node and relation types that captures composite relationships between nodes. For example, in an academic network, meta-paths can describe relationships such as two authors co-authoring a paper (AP) or two papers authored by the same individual (PA). Meta-paths help decompose the complexity of HGNNs into multiple semantic modules [2]. However, real-world semantics are often intricate and involve higher-order relationships.

For instance, the meta-path APTPA can represent two sets of authors writing papers on the same topic. Such high-order semantics are challenging to capture using simple, low-order meta-paths alone. Although meta-path-based methods for HGNN learning often outperform traditional network embedding techniques in various tasks [3], they still have notable limitations. For example, HAN [4] employs a hierarchical attention mechanism to aggregate homogeneous subgraphs derived from different meta-paths but lacks the ability to handle complex highorder semantics, restricting it to capturing pairwise relationships. ESim [5] utilizes contrastive learning by treating node pairs within a meta-path instance as positive samples but fails to fully leverage the semantic information of intermediate nodes, resulting in incomplete feature representations. HERec [6] converts HGNNs into homogeneous subgraphs based on meta-paths and aggregates multi-semantic information but, like ESim, primarily focuses on relationships between endpoint nodes, neglecting intermediate nodes. Additionally, methods like Heco [7] and STENCIL [8] enhance both semantic and structural modeling but still fall short in supporting higher-order semantics fully. While MHNF [9] improves the capture of higher-order semantics, it still faces challenges related to computational efficiency and multisemantic conflicts. To address the limitations of existing methods, we

identify three key aspects that require focus when learning HGNNs representations based on meta-paths:

(1) The scope of meta-paths: Can the method capture complex high-order semantics to comprehensively reveal deep relationships within the graph?

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