Meta-structure-based graph attention networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Graph Neural Networks (GNNs) excel at node classification and link prediction on homogeneous graphs.
• Heterogeneous networks present challenges due to diverse node and edge types, limiting existing GNN models.
• Current heterogeneous GNNs often require predefined meta-structures (meta-paths, meta-graphs), overlooking their varying impact.

Purpose of the Study:

• To propose MS-GAN, a novel Graph Neural Network model for effective representation learning in heterogeneous networks.
• To automatically generate and weight relevant meta-structures, overcoming limitations of predefined approaches.
• To enhance downstream task performance by adapting to the semantic richness of heterogeneous graphs.

Main Methods:

• MS-GAN comprises four components: graph structure learner, expander, filter, and parser.
• The graph structure learner uses 1x1 convolution to generate meta-paths from sub-adjacent matrices.
• Meta-graphs are generated via Hadamard product, filtered for effectiveness, and weighted using semantic hierarchical attention.

Main Results:

• MS-GAN automatically generates effective meta-structures for heterogeneous graph representation learning.
• The model assigns differential weights to various meta-structures based on their semantic importance.
• Experiments on four datasets demonstrate MS-GAN's superior performance and provide meta-structure visualization insights.

Conclusions:

• MS-GAN successfully addresses the challenges of representation learning in heterogeneous networks.
• The automatic generation and weighting of meta-structures by MS-GAN offer a significant advancement.
• This approach enhances the adaptability and performance of GNNs on complex, real-world graph data.