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Documents authored by Scholtes, Ingo

Document
DOI: 10.4230/DagRep.11.7.139
Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)

Authors: Tina Eliassi-Rad, Vito Latora, Martin Rosvall, and Ingo Scholtes

Published in: Dagstuhl Reports, Volume 11, Issue 7 (2021)

Abstract
Graph and network models are essential for data science applications in computer science, social sciences, and life sciences. They help to detect patterns in data on dyadic relations between pairs of genes, humans, or documents, and have improved our understanding of complex networks across disciplines. While the advantages of graph models of relational data are undisputed, we often have access to data with multiple types of higher-order relations not captured by simple graphs. Such data arise in social systems with non-dyadic or group-based interactions, multi-modal transportation networks with multiple connection types, or time series containing specific sequences of nodes traversed on paths. The complex relational structure of such data questions the validity of graph-based data mining and modelling, and jeopardises interdisciplinary applications of network analysis and machine learning. To address this challenge, researchers in topological data analysis, network science, machine learning, and physics recently started to generalise network analysis to higher-order graph models that capture more than dyadic relations. These higher-order models differ from standard network analysis in assumptions, applications, and mathematical formalisms. As a result, the emerging field lacks a shared terminology, common challenges, benchmark data and metrics to facilitate fair comparisons. By bringing together researchers from different disciplines, Dagstuhl Seminar 21352 "Higher-Order Graph Models: From Theoretical Foundations to Machine Learning" aimed at the development of a common language and a shared understanding of key challenges in the field that foster progress in data analytics and machine learning for data with complex relational structure. This report documents the program and the outcomes of this seminar.
Cite as

Tina Eliassi-Rad, Vito Latora, Martin Rosvall, and Ingo Scholtes. Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352). In Dagstuhl Reports, Volume 11, Issue 7, pp. 139-178, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@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}
}
Document
DOI: 10.4230/OASIcs.ICCSW.2012.116
A Quantitative Study of Social Organisation in Open Source Software Communities

Authors: Marcelo Serrano Zanetti, Emre Sarigöl, Ingo Scholtes, Claudio Juan Tessone, and Frank Schweitzer

Published in: OASIcs, Volume 28, 2012 Imperial College Computing Student Workshop

Abstract
The success of open source projects crucially depends on the voluntary contributions of a sufficiently large community of users. Apart from the mere size of the community, interesting questions arise when looking at the evolution of structural features of collaborations between community members. In this article, we discuss several network analytic proxies that can be used to quantify different aspects of the social organisation in social collaboration networks. We particularly focus on measures that can be related to the cohesiveness of the communities, the distribution of responsibilities and the resilience against turnover of community members. We present a comparative analysis on a large-scale dataset that covers the full history of collaborations between users of $14$ major open source software communities. Our analysis covers both aggregate and time-evolving measures and highlights differences in the social organisation across communities. We argue that our results are a promising step towards the definition of suitable, potentially multi-dimensional, resilience and risk indicators for open source software communities.
Cite as

Marcelo Serrano Zanetti, Emre Sarigöl, Ingo Scholtes, Claudio Juan Tessone, and Frank Schweitzer. A Quantitative Study of Social Organisation in Open Source Software Communities. In 2012 Imperial College Computing Student Workshop. Open Access Series in Informatics (OASIcs), Volume 28, pp. 116-122, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{serranozanetti_et_al:OASIcs.ICCSW.2012.116,
  author =	{Serrano Zanetti, Marcelo and Sarig\"{o}l, Emre and Scholtes, Ingo and Tessone, Claudio Juan and Schweitzer, Frank},
  title =	{{A Quantitative Study of Social Organisation in Open Source Software Communities}},
  booktitle =	{2012 Imperial College Computing Student Workshop},
  pages =	{116--122},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-48-4},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{28},
  editor =	{Jones, Andrew V.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2012.116},
  URN =		{urn:nbn:de:0030-drops-37748},
  doi =		{10.4230/OASIcs.ICCSW.2012.116},
  annote =	{Keywords: open source software, mining software repositories, social networks}
}
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