DGNN: Decoupled Graph Neural Networks with Structural Consistency
  between Attribute and Graph Embedding Representations
Jinlu Wang Jipeng Guo Yanfeng Sun Junbin Gao Shaofan Wang Yachao Yang Baocai Yin
Graph neural networks (GNNs) demonstrate a robust capability for
representation learning on graphs with complex structures, showcasing superior
performance in various applications. The majority of existing GNNs employ a
graph convolution operation by using both attribute and structure information
through coupled learning. In essence, GNNs, from an optimization perspective,
seek to learn a consensus and compromise embedding representation that balances
attribute and graph information, selectively exploring and retaining valid
information. To obtain a more comprehensive embedding representation of nodes,
a novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is
introduced. DGNN explores distinctive embedding representations from the
attribute and graph spaces by decoupled terms. Considering that semantic graph,
constructed from attribute feature space, consists of different node connection
information and provides enhancement for the topological graph, both
topological and semantic graphs are combined for the embedding representation
learning. Further, structural consistency among attribute embedding and graph
embeddings is promoted to effectively remove redundant information and
establish soft connection. This involves promoting factor sharing for adjacency
reconstruction matrices, facilitating the exploration of a consensus and
high-level correlation. Finally, a more powerful and complete representation is
achieved through the concatenation of these embeddings. Experimental results
conducted on several graph benchmark datasets verify its superiority in node
classification task.