{"url":"/method/pgc-dgcnn","slug":"pgc-dgcnn","name":"PGC-DGCNN","full_name":"PGC-DGCNN","full_name_withheld":false,"description_markdown":"PGC-DGCNN provides a new definition of graph convolutional filter. It generalizes the most commonly adopted filter, adding an hyper-parameter controlling the distance of the considered neighborhood. The model extends graph convolutions, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. The methods involves a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph.\r\n\r\nDescription and image from: [On Filter Size in Graph Convolutional Networks](https://arxiv.org/pdf/1811.10435.pdf)","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1811.10435v1","title":"On Filter Size in Graph Convolutional Networks","url_on_a_paper_host":true},"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":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/on-filter-size-in-graph-convolutional","title":"On Filter Size in Graph Convolutional Networks","date":"2018-11-23","arxiv_id":"1811.10435","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[{"year":"2018","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/pgc-dgcnn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}