DagRep.13.12.1.pdf
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Scalable Graph Mining and Learning (Dagstuhl Seminar 23491)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 13, Issue 12
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-04-24
Cite As Get BibTex
Danai Koutra, Henning Meyerhenke, Ilya Safro, and Fabian Brandt-Tumescheit. Scalable Graph Mining and Learning (Dagstuhl Seminar 23491). In Dagstuhl Reports, Volume 13, Issue 12, pp. 1-23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/DagRep.13.12.1
Abstract
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.
Subject Classification
ACM Subject Classification
- Theory of computation → Graph algorithms analysis
- Computing methodologies → Machine learning algorithms
- Computing methodologies → Parallel algorithms
Keywords
- Graph mining
- Graph machine learning
- (hyper)graph and network algorithms
- high-performance computing for graphs
- algorithm engineering for graphs
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