Visual Analysis of Evolution of EEG Coherence Networks employing Temporal Multidimensional Scaling
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The community structure of networks plays an important role in their analysis. It represents a high-level organization of objects within a network. However, in many application domains, the relationship between objects in a network changes over time, resulting in the change of community structure (the partition of a network), their attributes (the composition of a community and the values of relationships between communities), or both. Previous animation or timeline-based representations either visualize the change of attributes of networks or the community structure. There is no single method that can optimally show graphs that change in both structure and attributes. In this paper we propose a method for the case of dynamic EEG coherence networks to assist users in exploring the dynamic changes in both their community structure and their attributes. The method uses an initial timeline representation which was designed to provide an overview of changes in community structure. In addition, we order communities and assign colors to them based on their relationships by adapting the existing Temporal Multidimensional Scaling (TMDS) method. Users can identify evolution patterns of dynamic networks from this visualization.
Description
@inproceedings{10.2312:vcbm.20181233,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau},
title = {{Visual Analysis of Evolution of EEG Coherence Networks employing Temporal Multidimensional Scaling}},
author = {Ji, Chengtao and Maurits, Natasha M. and Roerdink, Jos B. T. M.},
year = {2018},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-056-7},
DOI = {10.2312/vcbm.20181233}
}