Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs

Understand

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc.

• However, most of the existing approaches start from the binary relationship (i.e., edges) in the graph and have not leveraged the higher order local structure (i.e., motifs) of the graph.
• Here, we propose mGCMN -- a novel framework which utilizes node feature information and the higher order local structure of the graph to effectively generate node embeddings for previously unseen data.
• Through research we have found that different types of networks have different key motifs.