Not logged in.

Quick Search - Contribution

Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to Its Embedding
Organization Unit
Authors
  • Zipeng Liu
  • Yang Wang
  • Jürgen Bernard
  • Tamara Munzner
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title IEEE Transactions on Visualization and Computer Graphics
Publisher Institute of Electrical and Electronics Engineers
Geographical Reach international
ISSN 1077-2626
Volume 28
Number 6
Page Range 2500 - 2516
Date 2022
Abstract Text Graph neural networks (GNNs) are a class of powerful machine learning tools that model node relations for making predictions of nodes or links. GNN developers rely on quantitative metrics of the predictions to evaluate a GNN, but similar to many other neural networks, it is difficult for them to understand if the GNN truly learns characteristics of a graph as expected. We propose an approach to corresponding an input graph to its node embedding (aka latent space), a common component of GNNs that is later used for prediction. We abstract the data and tasks, and develop an interactive multi-view interface called CorGIE to instantiate the abstraction. As the key function in CorGIE, we propose the K-hop graph layout to show topological neighbors in hops and their clustering structure. To evaluate the functionality and usability of CorGIE, we present how to use CorGIE in two usage scenarios, and conduct a case study with five GNN experts. Availability: Open-source code at https://github.com/zipengliu/corgie-ui/ , supplemental materials & video at https://osf.io/tr3sb/ .
Free access at Related URL
Related URLs
Digital Object Identifier 10.1109/TVCG.2022.3148197
Other Identification Number merlin-id:23091
PDF File Download
Export BibTeX
EP3 XML (ZORA)
Keywords Training, Motion pictures, Task analysis,Computational modeling, Aggregates, Pipelines, Layout