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One of the building blocks of most analysis in visual arts is to find similarity relationships among paintings of different artists and painting schools. To help art historians better understand visual arts, this paper presents a framework for <jats:italic>visual link retrieval<\/jats:italic> and <jats:italic>knowledge discovery<\/jats:italic> in digital painting datasets. Visual link retrieval is accomplished by using a deep convolutional neural network to perform feature extraction and a fully unsupervised nearest neighbor mechanism to retrieve links among digitized paintings. <jats:italic>Historical<\/jats:italic> knowledge discovery is achieved by performing a graph analysis that makes it possible to study influences among artists. An experimental evaluation on a database collecting paintings by very popular artists shows the effectiveness of the method. The unsupervised strategy makes the method interesting especially in cases where metadata are scarce, unavailable or difficult to collect.<\/jats:p>","DOI":"10.1007\/s11042-020-09995-z","type":"journal-article","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T03:25:57Z","timestamp":1602991557000},"page":"6599-6616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Visual link retrieval and knowledge discovery in painting datasets"],"prefix":"10.1007","volume":"80","author":[{"given":"Giovanna","family":"Castellano","sequence":"first","affiliation":[]},{"given":"Eufemia","family":"Lella","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0883-2691","authenticated-orcid":false,"given":"Gennaro","family":"Vessio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"issue":"1","key":"9995_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s10489-018-1179-1","volume":"49","author":"UR Acharya","year":"2019","unstructured":"Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M, SanTan R (2019) Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. 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