2026-07-23T03:46:26Z https://drops.dagstuhl.de/oai

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Scalable Graph Mining and Learning (Dagstuhl Seminar 23491) Koutra, Danai Meyerhenke, Henning Safro, Ilya Brandt-Tumescheit, Fabian Graph mining Graph machine learning (hyper)graph and network algorithms high-performance computing for graphs algorithm engineering for graphs This report documents the program and the outcomes of Dagstuhl Seminar 23491 "Scalable Graph Mining and Learning". The event brought together leading researchers and practitioners to discuss cutting-edge developments in graph machine learning, massive-scale graph analytics frameworks, and optimization techniques for graph processing. Besides the executive summary, the report contains abstracts of the 18 scientific talks presented, descriptions of three open problems, and preliminary results of three working groups formed during the seminar. In summary, the seminar successfully fostered discussions that bridged theoretical research and practical applications in scalable graph learning, mining, and analytics. Several potential outcomes include writing position and research papers as well as joint grant submissions. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Danai Koutra and Henning Meyerhenke and Ilya Safro and Fabian Brandt-Tumescheit 2024 Is Part Of Dagstuhl Reports, Volume 13, Issue 12 (2024) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.13.12.1 urn:nbn:de:0030-drops-198530 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.12.1 eng https://creativecommons.org/licenses/by/4.0/legalcode