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Graph neural networks can be effectively applied to find solutions for manyreal-world problems across widely diverse fields. The success of graph neuralnetworks is linked to the message-passing mechanism on the graph, however, themessage-aggregating behavior is still not entirely clear in most algorithms. Toimprove functionality, we propose a new transparent network called GraphDecipher to investigate the message-passing mechanism by prioritizing in twomain components: the graph structure and node attributes, at the graph,feature, and global levels on a graph under the node classification task.However, the computation burden now becomes the most significant issue becausethe relevance of both graph structure and node attributes are computed on agraph. In order to solve this issue, only relevant representative nodeattributes are extracted by graph feature filters, allowing calculations to beperformed in a category-oriented manner. Experiments on seven datasets showthat Graph Decipher achieves state-of-the-art performance while imposing asubstantially lower computation burden under the node classification task.Additionally, since our algorithm has the ability to explore the representativenode attributes by category, it is utilized to alleviate the imbalanced nodeclassification problem on multi-class graph datasets.

Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification