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Associative Learning for Network Embedding
[article]
2022
arXiv
pre-print
The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverages Modern Hopfield Networks (MHN) for associative learning. Our network learns associations between the content of each node and that
arXiv:2208.14376v1
fatcat:bph5ttfapzbwvdpwllyyuha24u