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Visualisation of Temporal Network Data via Time-Aware Static Representations with HOTVis

Authors:

Vincenzo Perri,

Ingo ScholtesAuthors Info & Claims

WWW '21: Companion Proceedings of the Web Conference 2021

Pages 533 - 534

https://doi.org/10.1145/3442442.3452053

Published: 03 June 2021 Publication History

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Abstract

The visual analysis of temporal network data is often hindered by the cognitively demanding nature of dynamic graphic visualizations. Addressing this issue, the graph visualization tool HOTVis generates time-aware static network visualizations that highlight the causal topology of temporal networks, i.e. which nodes can directly and indirectly influence each other, and are thus considerably easier to interpret than state-of-the-art dynamic graph visualizations.

References

[1]

Thomas MJ Fruchterman and Edward M Reingold. 1991. Graph drawing by force-directed placement. Software: Practice and experience 21, 11 (1991), 1129–1164.

Google Scholar

[2]

Petter Holme. 2015. Modern temporal network theory: a colloquium. The European Physical Journal B 88, 9 (2015), 234.

Crossref

Google Scholar

[3]

David Kempe, Jon Kleinberg, and Amit Kumar. 2002. Connectivity and inference problems for temporal networks. J. Comput. System Sci. 64, 4, 820–842.

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Google Scholar

[4]

Renaud Lambiotte, Martin Rosvall, and Ingo Scholtes. 2019. From networks to optimal higher-order models of complex systems. Nature physics 15, 4 (2019), 313–320.

Google Scholar

[5]

Vincenzo Perri and Ingo Scholtes. 2020. HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs. In Graph Drawing and Network Visualization. 99–114.

Google Scholar

[6]

Vincenzo Perri and Ingo Scholtes. 2020. HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs (Supplementary Code and Data). https://doi.org/10.5281/zenodo.3994152

Crossref

Google Scholar

[7]

Ingo Scholtes. 2017. When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks. KDD, 1037–1046.

Google Scholar

Cited By

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Arregui-García BLonga ALotito QMeloni SCencetti G(2024)Patterns in Temporal Networks with Higher-Order Egocentric StructuresEntropy10.3390/e2603025626:3(256)Online publication date: 13-Mar-2024
https://doi.org/10.3390/e26030256
Ahlers DWilde ESpaniol MBaeza-Yates RAlonso O(2022)Report on the 11th international workshop on location and the web (LocWeb 2021) and the 11th temporal web analytics workshop (TempWeb2021) at WWW2021ACM SIGIR Forum10.1145/3527546.352755555:2(1-7)Online publication date: 17-Mar-2022
https://dl.acm.org/doi/10.1145/3527546.3527555

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Published In

WWW '21: Companion Proceedings of the Web Conference 2021

April 2021

726 pages

ISBN:9781450383134

DOI:10.1145/3442442

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
Wikimedia Foundation

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 03 June 2021

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WWW '21: The Web Conference 2021

April 19 - 23, 2021

Ljubljana, Slovenia

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Arregui-García BLonga ALotito QMeloni SCencetti G(2024)Patterns in Temporal Networks with Higher-Order Egocentric StructuresEntropy10.3390/e2603025626:3(256)Online publication date: 13-Mar-2024
https://doi.org/10.3390/e26030256
Ahlers DWilde ESpaniol MBaeza-Yates RAlonso O(2022)Report on the 11th international workshop on location and the web (LocWeb 2021) and the 11th temporal web analytics workshop (TempWeb2021) at WWW2021ACM SIGIR Forum10.1145/3527546.352755555:2(1-7)Online publication date: 17-Mar-2022
https://dl.acm.org/doi/10.1145/3527546.3527555

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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet

HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs

A developing framework for interactive temporal data visualization

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