{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discrete-latent-graph-generative-modeling","title":"GLAD: Improving Latent Graph Generative Modeling with Simple Quantization","arxiv_id":"2403.16883","date":"2024-03-25","proceeding":null,"authors":["Van Khoa Nguyen","Yoann Boget","Frantzeska Lavda","Alexandros Kalousis"],"abstract":"Exploring the graph latent structures has not garnered much attention in the graph generative research field. Yet, exploiting the latent space is as crucial as working on the data space for discrete data such as graphs. 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