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DOI: 10.4230/DagRep.12.3.141
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132)

Authors: Martin Grohe, Stephan Günnemann, Stefanie Jegelka, and Christopher Morris

Published in: Dagstuhl Reports, Volume 12, Issue 3 (2022)

Abstract
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.
Cite as

Martin Grohe, Stephan Günnemann, Stefanie Jegelka, and Christopher Morris. Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). In Dagstuhl Reports, Volume 12, Issue 3, pp. 141-155, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{grohe_et_al:DagRep.12.3.141,
  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 =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.3.141},
  URN =		{urn:nbn:de:0030-drops-172727},
  doi =		{10.4230/DagRep.12.3.141},
  annote =	{Keywords: Machine Learning For Graphs, GNNs, Graph Embedding}
}
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