We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.
%0 Conference Paper
%1 conf/iv/BruderHFBWE18
%A Bruder, Valentin
%A Hlawatsch, Marcel
%A Frey, Steffen
%A Burch, Michael
%A Weiskopf, Daniel
%A Ertl, Thomas
%B Proceedings of the International Conference Information Visualisation (IV)
%D 2018
%E Banissi, Ebad
%E Francese, Rita
%E Bannatyne, Mark W. McK.
%E Wyeld, Theodor G.
%E Sarfraz, Muhammad
%E Pires, João Moura
%E Ursyn, Anna
%E Bouali, Fatma
%E Datia, Nuno
%E Venturini, Gilles
%E Polese, Giuseppe
%E Deufemia, Vincenzo
%E Mascio, Tania Di
%E Temperini, Marco
%E Sciarrone, Filippo
%E Malandrino, Delfina
%E Zaccagnino, Rocco
%E Díaz, Paloma
%E Papadopoulo, Fragkiskos
%E Anta, Antonio Fernández
%E Cuzzocrea, Alfredo
%E Risi, Michele
%E Erra, Ugo
%E Rossano, Veronica
%I IEEE
%K from:leonkokkoliadis visus:hlawatml A02 sfbtrr161 visus:brudervn visus:burchml visus:ertl visus visus:weiskopf 2018 from:mueller visus:freysn
%P 210-219
%R 10.1109/iV.2018.00045
%T Volume-Based Large Dynamic Graph Analytics
%U https://ieeexplore.ieee.org/document/8564163
%X We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.
%@ 978-1-5386-7202-0
@inproceedings{conf/iv/BruderHFBWE18,
abstract = {We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.},
added-at = {2020-10-09T12:31:49.000+0200},
author = {Bruder, Valentin and Hlawatsch, Marcel and Frey, Steffen and Burch, Michael and Weiskopf, Daniel and Ertl, Thomas},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2dfa242805be650334eb0c8dc76602348/visus},
booktitle = {Proceedings of the International Conference Information Visualisation (IV)},
crossref = {conf/iv/2018},
description = {Volume-Based Large Dynamic Graph Analytics},
doi = {10.1109/iV.2018.00045},
editor = {Banissi, Ebad and Francese, Rita and Bannatyne, Mark W. McK. and Wyeld, Theodor G. and Sarfraz, Muhammad and Pires, João Moura and Ursyn, Anna and Bouali, Fatma and Datia, Nuno and Venturini, Gilles and Polese, Giuseppe and Deufemia, Vincenzo and Mascio, Tania Di and Temperini, Marco and Sciarrone, Filippo and Malandrino, Delfina and Zaccagnino, Rocco and Díaz, Paloma and Papadopoulo, Fragkiskos and Anta, Antonio Fernández and Cuzzocrea, Alfredo and Risi, Michele and Erra, Ugo and Rossano, Veronica},
ee = {http://doi.ieeecomputersociety.org/10.1109/iV.2018.00045},
interhash = {c4e8eb0482b7a4b0104ed1f4c483708e},
intrahash = {dfa242805be650334eb0c8dc76602348},
isbn = {978-1-5386-7202-0},
keywords = {from:leonkokkoliadis visus:hlawatml A02 sfbtrr161 visus:brudervn visus:burchml visus:ertl visus visus:weiskopf 2018 from:mueller visus:freysn},
pages = {210-219},
publisher = {IEEE},
timestamp = {2020-10-09T10:31:49.000+0200},
title = {Volume-Based Large Dynamic Graph Analytics},
url = {https://ieeexplore.ieee.org/document/8564163},
year = 2018
}