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On the Equivalence between Positional Node Embeddings and Structural Graph Representations
[article]
2020
arXiv
pre-print
This work provides the first unifying theoretical framework for node (positional) embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that the relationship between structural representations and node embeddings is analogous to that of a distribution and its samples. We prove that all tasks that can be performed by node embeddings can also be performed by structural representations and vice-versa.
arXiv:1910.00452v3
fatcat:4bx2573owvbephefgwp36i5yiu