AUTHOR=Liang Yuchen , Krotov Dmitry , Zaki Mohammed J. TITLE=Modern Hopfield Networks for graph embedding JOURNAL=Frontiers in Big Data VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.1044709 DOI=10.3389/fdata.2022.1044709 ISSN=2624-909X ABSTRACT=

The network embedding task is to represent a node in a 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 node's neighbors. These associations serve as memories in the MHN. The recurrent dynamics of the network make it possible to recover the masked node, given that node's neighbors. Our proposed method is evaluated on different benchmark datasets for downstream tasks such as node classification, link prediction, and graph coarsening. The results show competitive performance compared to the common matrix factorization techniques and deep learning based methods.