Heterogeneous Graph Embedding with Dual Edge Differentiation.

Area of Science:

• Graph Representation Learning
• Machine Learning
• Data Mining

Background:

• Heterogeneous graphs are crucial for modeling real-world data with diverse entities and relations.
• Existing meta-path-based methods often overlook the diversity in meta-path types and ranges, limiting semantic learning.
• The fixed nature of pre-computed paths in traditional methods neglects nuanced node correlations.

Purpose of the Study:

• To propose a novel meta-path-based semantic embedding schema, Heterogeneous Graph Embedding with Dual Edge Differentiation (HGE-DED).
• To enhance the learning of rich and discriminative node semantics by constructing flexible meta-path combinations.
• To address the limitations of fixed meta-path construction in existing heterogeneous graph embedding methods.

Main Methods:

• Developed Multi-Type and multi-Range Meta-Path Construction (MTR-MP Construction) for comprehensive meta-path exploration.
• Incorporated semantics and meta-path joint guidance for hierarchical short- and long-range relation adjustment.
• Designed a dual edge differentiation mechanism to better represent edge diversity at fine-grained scales.

Main Results:

• HGE-DED effectively constructs flexible meta-path combinations, capturing diverse semantic information.
• The method demonstrates superior performance in learning discriminative node embeddings compared to state-of-the-art approaches.
• Experimental results on four benchmark datasets validate the effectiveness of the proposed HGE-DED schema.

Conclusions:

• HGE-DED offers a more robust approach to heterogeneous graph embedding by embracing meta-path diversity.
• The proposed method successfully mitigates the impact of edge heterophily in heterogeneous graphs.
• This work advances the field of graph representation learning by providing a more nuanced understanding of heterogeneous graph structures.