BibTeX Export for Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)

Copy to Clipboard Download

@Article{eliassirad_et_al:DagRep.11.7.139,
  author =	{Eliassi-Rad, Tina and Latora, Vito and Rosvall, Martin and Scholtes, Ingo},
  title =	{{Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)}},
  pages =	{139--178},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{11},
  number =	{7},
  editor =	{Eliassi-Rad, Tina and Latora, Vito and Rosvall, Martin and Scholtes, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.139},
  URN =		{urn:nbn:de:0030-drops-155929},
  doi =		{10.4230/DagRep.11.7.139},
  annote =	{Keywords: (Social) Network analysis, Graph mining, Graph theory, Network science, Machine Learning, Statistical relational learning, Topological data analysis}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode