  Graph neural networks can be effectively applied to find solutions for many
real-world problems across widely diverse fields. The success of graph neural
networks is linked to the message-passing mechanism on the graph, however, the
message-aggregating behavior is still not entirely clear in most algorithms. To
improve functionality, we propose a new transparent network called Graph
Decipher to investigate the message-passing mechanism by prioritizing in two
main 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 because
the relevance of both graph structure and node attributes are computed on a
graph. In order to solve this issue, only relevant representative node
attributes are extracted by graph feature filters, allowing calculations to be
performed in a category-oriented manner. Experiments on seven datasets show
that Graph Decipher achieves state-of-the-art performance while imposing a
substantially lower computation burden under the node classification task.
Additionally, since our algorithm has the ability to explore the representative
node attributes by category, it is utilized to alleviate the imbalanced node
classification problem on multi-class graph datasets.
