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Documents authored by Latora, Vito

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/DagRep.1.11.39
Analysis of Dynamic Social and Technological Networks (Dagstuhl Seminar 11452)

Authors: Vito Latora, Cecilia Mascolo, and Mirco Musolesi

Published in: Dagstuhl Reports, Volume 1, Issue 11 (2012)

Abstract
With the growing popularity of online communication tools, researchers have turned their attention to the study of the networks arising between users of social networking services, between mobile phone callers and, in general, between individuals connected by technological means. Thanks to the rich set of techniques and methods developed by complex network science, and joining forces with sociologists and psychologists, the analysis of dynamic social and technological networks has sparked many important results, attracting even more interest as the importance of such systems grows over time. This Dagstuhl seminar brought together researchers and practitioners from computer science, physics and psychology, covering the diverse areas of social and technological network analysis. The goal of the seminar was to bring together people from different areas of expertise, focusing on both mathematical aspects and practical applications of theoretical models and techniques. In particular, the evolution of this research field and of its future perspectives was a major theme of the seminar. This seminar was attended by 25 participants.
Cite as

Vito Latora, Cecilia Mascolo, and Mirco Musolesi. Analysis of Dynamic Social and Technological Networks (Dagstuhl Seminar 11452). In Dagstuhl Reports, Volume 1, Issue 11, pp. 39-49, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

Copy BibTex To Clipboard

@Article{latora_et_al:DagRep.1.11.39,
  author =	{Latora, Vito and Mascolo, Cecilia and Musolesi, Mirco},
  title =	{{Analysis of Dynamic Social and Technological Networks (Dagstuhl Seminar 11452)}},
  pages =	{39--49},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2012},
  volume =	{1},
  number =	{11},
  editor =	{Latora, Vito and Mascolo, Cecilia and Musolesi, Mirco},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.1.11.39},
  URN =		{urn:nbn:de:0030-drops-33744},
  doi =		{10.4230/DagRep.1.11.39},
  annote =	{Keywords: complex networks, network analysis, network data mining}
}
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