FairExpand: Individual Fairness on Graphs with Partial Similarity Information

This article introduces FairExpand, a method for addressing individual fairness in graph-based machine learning, particularly when only partial similarity information is available. The focus on fairness and the handling of incomplete data are key contributions. The use of graphs suggests applications in areas like social networks or recommendation systems. Further analysis would require examining the specific techniques used and the evaluation metrics employed.

• •Addresses individual fairness in graph-based machine learning.
• •Handles scenarios with partial similarity information.
• •Suggests applications in social networks and recommendation systems.