Learning the Implicit Semantic Representation on Graph-Structured Data. (arXiv:2101.06471v1 [cs.AI])

Existing representation learning methods in graph convolutional networks are
mainly designed by describing the neighborhood of each node as a perceptual
whole, while the implicit semantic associations behind highly complex
interactions of graphs are largely unexploited. In this paper, we propose a
Semantic Graph Convolutional Networks (SGCN) that explores the implicit
semantics by learning latent semantic-paths in graphs. In previous work, there
are explorations of graph semantics via meta-paths. However, these methods
mainly rely on explicit heterogeneous information that is hard to be obtained
in a large amount of graph-structured data. SGCN first breaks through this
restriction via leveraging the semantic-paths dynamically and automatically
during the node aggregating process. To evaluate our idea, we conduct
sufficient experiments on several standard datasets, and the empirical results
show the superior performance of our model.

Source: https://arxiv.org/abs/2101.06471


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