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In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named<jats:sc>KPlexPool<\/jats:sc>, builds on the concepts of graph covers and<jats:italic>k<\/jats:italic>-plexes, i.e. pseudo-cliques where each node can miss up to<jats:italic>k<\/jats:italic>links. The experimental evaluation on benchmarks on molecular and social graph classification shows that<jats:sc>KPlexPool<\/jats:sc>achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information.<\/jats:p>","DOI":"10.1007\/s10618-021-00779-z","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T06:03:03Z","timestamp":1628661783000},"page":"2200-2220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["K-plex cover pooling for graph neural networks"],"prefix":"10.1007","volume":"35","author":[{"given":"Davide","family":"Bacciu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessio","family":"Conte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Grossi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5674-0663","authenticated-orcid":false,"given":"Francesco","family":"Landolfi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Marino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"779_CR1","doi-asserted-by":"publisher","unstructured":"Bacciu D, Di\u00a0Sotto L (2019) A non-negative factorization approach to node pooling in graph convolutional neural networks. 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