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Accurate Learning of Graph Representations with Graph Multiset Pooling
[article]
2021
arXiv
pre-print
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or average over all node representations considers all node features equally without consideration of their task relevance, and any structural dependencies among them. Recently proposed
arXiv:2102.11533v4
fatcat:qdqktuojbbbx7ofm6dgqzmevx4