Understanding Indirect Causal Relationships in Node‐Link Graphs-Reference-Cited by-同舟云学术

Understanding Indirect Causal Relationships in Node‐Link Graphs

Published:2017-06 Issue:3 Volume:36 Page:411-421
ISSN:0167-7055
Container-title:Computer Graphics Forum
language:en
Short-container-title:Computer Graphics Forum

Author:

Bae Juhee¹,Helldin Tove¹,Riveiro Maria¹

Affiliation:

1. School of InformaticsUniversity of Skövde Skövde Sweden

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13198

Reference28 articles.

1. Alimadadi JaniS.:Propagation of change and visualization of causality in dependency structures 2013.2

2. On the effective visualisation of dynamic attribute cascades

3. Conservative forgetful scholars: How people learn causal structure through sequences of interventions.

4. BaeJ. VentocillaE. RiveiroM. HelldinT. Falk‐manG.:Evaluating multi‐attributes on cause and effect relationship visualization. In The 8th International Conference on Information Visualization Theory and Applications (IVAPP)(2017).2 3 5 8 10

5. From Data Analysis and Visualization to Causality Discovery

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Causality-Based Visual Analysis of Questionnaire Responses;IEEE Transactions on Visualization and Computer Graphics;2023

2. ViSRE: A Unified Visual Analysis Dashboard for Proactive Cloud Outage Management;2022 Working Conference on Software Visualization (VISSOFT);2022-10

3. DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena;IEEE Transactions on Visualization and Computer Graphics;2022

4. Compass: Towards Better Causal Analysis of Urban Time Series;IEEE Transactions on Visualization and Computer Graphics;2022-01

5. Visual Assistance in Development and Validation of Bayesian Networks for Clinical Decision Support;IEEE Transactions on Visualization and Computer Graphics;2022