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Existing approaches either partition the graph into disjoint,<jats:italic>non-overlapping<\/jats:italic>, communities, or determine only<jats:italic>overlapping<\/jats:italic>communities. To date, no method supports both detections of overlapping and non-overlapping communities. We propose UCoDe, a<jats:italic>unified<\/jats:italic>method for community detection in attributed graphs that detects both overlapping and non-overlapping communities by means of a novel contrastive loss that captures node similarity on a macro-scale. 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Juelich research center (fz-juelich.de). Ira Assent: Co-author. laria Bordino: Recent collaborator. rancesco Gullo: Recent collaborator. Panagiotis Karras: Colleague. Thomas Seidl: PhD advisor.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors provide the appropriate consent to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors provide the consent to publish the images in the manuscript. The data used in the publication is publicly available. We provide respective citations for each of the data sources.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}