Tutorial Fairness In Graph Mining

By Editorial Team · October 19, 2026 · 4 min read

Tutorial Fairness In Graph Mining Information Guide

  1. Background of Tutorial Fairness In Graph Mining
  2. Important Facts
  3. Recent Updates
  4. Deep Dive
  5. Future Outlook

Background of Tutorial Fairness In Graph Mining

Full Tutorial: Fairness in Graph Mining News
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Important Facts

Information #01 - Yushun Dong (University of Virginia) - Fairness in Graph Mining: Metrics and Algorithms News
Explore the main sources for Tutorial Fairness In Graph Mining.

Recent Updates

Details #04 - Jing Ma (University of Virginia) - Fair Node Representation with Graph Counterfactual Fairness Guide
Stay updated on Tutorial Fairness In Graph Mining's latest milestones.

FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models
FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models
#07 - Jian Kang (UIUC) - Algorithmic Foundation of Fair Graph Mining
#07 - Jian Kang (UIUC) - Algorithmic Foundation of Fair Graph Mining
Tutorial: Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned (KDD 2019)
Tutorial: Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned (KDD 2019)
KDD 2025 - Towards Controllable Hybrid Fairness in Graph Neural Networks
KDD 2025 - Towards Controllable Hybrid Fairness in Graph Neural Networks
Graph Mining Algorithm
Graph Mining Algorithm
Fairness in Graph Mining A Survey
Fairness in Graph Mining A Survey
KDD 2020: Lecture Style Tutorials: Fairness in Machine Learning for Healthcare
KDD 2020: Lecture Style Tutorials: Fairness in Machine Learning for Healthcare
Tutorial: Graph Rewiring: From Theory to Applications in Fairness
Tutorial: Graph Rewiring: From Theory to Applications in Fairness
KDD 2020: Hands-on Tutorials: Dealing with Bias and Fairness in Data Science Systems
KDD 2020: Hands-on Tutorials: Dealing with Bias and Fairness in Data Science Systems
FairRankVis A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models
FairRankVis A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models
Tutorial: 21 fairness definitions and their politics
Tutorial: 21 fairness definitions and their politics

Deep Dive

Data is compiled from public records and verified media reports.

Last Updated: October 19, 2026

Future Outlook

Full FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models Guide
For 2026, Tutorial Fairness In Graph Mining remains one of the most talked-about information profiles. Check back for the newest reports.

Disclaimer: Disclaimer: All information is compiled from publicly available data, media reports, and analysis. Actual details may vary.

Summary

Speaker: Daniel Kaszynski, SGH Warsaw School of Economics Friday, June 19th, 2026 ... ... references which includes this paper and also a survey paper in our group about IEEE VIS 2021 Presentation "FairRankVis: A Visual Analytics Framework for Exploring Algorithmic The presentation video of "FairRankVis: A Visual Analytics Framework for Exploring Algorithmic ... the input graph and why does the Zihan Luo:Huazhong University of Science and Technology;Hong Huang:Huazhong University of Science and Technology ... Muhammad A Ahmad (KenSci / University of Washington Tacoma); Arpit Patel (Univerisity of Washington); Carly Miller (University ... Organizers: Adrian Arnaiz-Rodriguez, Francisco Escolano, and Nuria Oliver Abstract: FairRankVis A Visual Analytics Framework for Exploring Algorithmic Computer scientists and statisticians have devised numerous mathematical criteria to define what it means for a classifier or a ...

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