{"url":"/method/nn4g","slug":"nn4g","name":"NN4G","full_name":"Neural network for graphs","full_name_withheld":false,"description_markdown":"NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data.\r\n\r\nDescription from: [Neural network for graphs: a contextual constructive approach](https://www.meta.org/papers/neural-network-for-graphs-a-contextual/19193509)","description_state":"present","introduced_year":2009,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Graphs","area_id":"graphs","collection":"Graph Models","url":"/methods/category/graph-models","pwc_aliases":[]}],"n_papers_tagged":0,"archive_num_papers":0,"papers_newest_first":[],"papers_shown":0,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/nn4g"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}