Latent neighborhood-based heterogeneous graph representation.

Area of Science:

• Computer Science
• Data Science
• Network Analysis

Background:

• Heterogeneous graphs effectively model complex systems with multi-modal and multi-typed interactions.
• Existing heterogeneous graph representation (HGR) methods often rely on meta-paths derived from direct neighbors, which can be insufficient.
• Inadequate direct neighbor information compromises the quality of meta-paths and subsequent HGR.

Purpose of the Study:

• To propose a novel HGR framework that addresses the limitations of meta-paths based on inadequate direct neighbor information.
• To enhance the discovery of semantic relationships in heterogeneous graphs by incorporating latent direct neighbors.
• To improve the accuracy of predictions in complex real-world networks.

Main Methods:

• Utilizing random walks to identify potential candidates from indirect neighbors.
• Employing HodgeRank to determine the importance and select latent direct neighbors.
• Augmenting neighborhood relationships with selected latent neighbors and refactoring the adjacency tensor.
• Adopting Graph Transformer Network for automatic semantic meta-path construction and HGR generation.

Main Results:

• The proposed framework generates a greater number of meta-path instances.
• It introduces more complex and diverse semantic information into the graph representation.
• The approach achieves more accurate predictions compared to state-of-the-art baselines on real-world heterogeneous networks.

Conclusions:

• The novel HGR framework effectively overcomes the limitations of traditional meta-path based methods.
• Incorporating latent direct neighbors significantly enhances the richness of semantic information in heterogeneous graphs.
• This method offers a promising direction for improving representation learning on complex, real-world networks.