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Improving Graph Neural Networks with Simple Architecture Design
[article]
2021
arXiv
pre-print
Graph Neural Networks have emerged as a useful tool to learn on the data by applying additional constraints based on the graph structure. These graphs are often created with assumed intrinsic relations between the entities. In recent years, there have been tremendous improvements in the architecture design, pushing the performance up in various prediction tasks. In general, these neural architectures combine layer depth and node feature aggregation steps. This makes it challenging to analyze
arXiv:2105.07634v1
fatcat:q4lxkvn2ffa75cwox24lrqndte