License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.3.141
URN: urn:nbn:de:0030-drops-172727
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Grohe, Martin ; G√ľnnemann, Stephan ; Jegelka, Stefanie ; Morris, Christopher
Weitere Beteiligte (Hrsg. etc.): Martin Grohe and Stephan G√ľnnemann and Stefanie Jegelka and Christopher Morris

Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132)

dagrep_v012_i003_p141_22132.pdf (4 MB)


Vectorial representations of graphs and relational structures, so-called graph embeddings, make it possible to apply standard tools from data mining, machine learning, and statistics to the graph domain. In particular, graph embeddings aim to capture important information about, both, the graph structure and available side information as a vector, to enable downstream tasks such as classification, regression, or clustering. Starting from the 1960s in chemoinformatics, research in various communities has resulted in a plethora of approaches, often with recurring ideas. However, most of the field advancements are driven by intuition and empiricism, often tailored to a specific application domain. Until recently, the area has received little stimulus from theoretical computer science, graph theory, and learning theory. The Dagstuhl Seminar 22132 "Graph Embeddings: Theory meets Practice", was aimed to gather leading applied and theoretical researchers in graph embeddings and adjacent areas, such as graph isomorphism, bio- and chemoinformatics, and graph theory, to stimulate an increased exchange of ideas between these communities.

BibTeX - Entry

  author =	{Grohe, Martin and G\"{u}nnemann, Stephan and Jegelka, Stefanie and Morris, Christopher},
  title =	{{Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132)}},
  pages =	{141--155},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{3},
  editor =	{Grohe, Martin and G\"{u}nnemann, Stephan and Jegelka, Stefanie and Morris, Christopher},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-172727},
  doi =		{10.4230/DagRep.12.3.141},
  annote =	{Keywords: Machine Learning For Graphs, GNNs, Graph Embedding}

Keywords: Machine Learning For Graphs, GNNs, Graph Embedding
Collection: DagRep, Volume 12, Issue 3
Issue Date: 2022
Date of publication: 14.11.2022

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