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001018835 005__ 20250929071104.0
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001018835 0247_ $$2datacite_doi$$a10.18154/RWTH-2025-08011
001018835 037__ $$aRWTH-2025-08011
001018835 041__ $$aEnglish
001018835 1001_ $$0P:(DE-82)IDM04795$$aFrantzen, Florian$$b0$$eCorresponding author$$urwth
001018835 1112_ $$a31. ACM SIGKDD Conference on Knowledge Discovery and Data Mining$$cToronto, ON$$d2025-08-03 - 2025-08-07$$gKDD 2025$$wCanada
001018835 245__ $$aHLSAD: Hodge Laplacian-based Simplicial Anomaly Detection$$honline
001018835 260__ $$aNew York, NY$$bAssociation for Computing Machinery$$c2025
001018835 29510 $$aKDD ’25 : Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
001018835 300__ $$a626-636
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001018835 7001_ $$0P:(DE-82)IDM03949$$aSchaub, Michael Thomas$$b1$$eCorresponding author$$urwth
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