Temporalizing static graph autoencoders to handle temporal networks

Autor: Haddad, Mounir, Bothorel, Cécile, Lenca, Philippe, Bedart, Dominique

Publikováno v: 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Nov 2021, The Hague, Netherlands

International audience; Graph autoencoders (GAE), also known as graph embedding methods, learn latent representations of the nodes of a graph in a low-dimensional space where the structural information is preserved. While real-world graphs are genera

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fb44965e56a986be4e68a0513a0f3cd
https://doi.org/10.1145/3487351.3488333