From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
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Abstract
This survey investigates the transition from conventional Euclidean data approaches in machine learning to collaborative learning methods that utilize graph-structured data.
Reality Card
The paper consolidates foundational principles of collaborative learning and introduces a taxonomy for graph-structured data, highlighting the need for standardized problem formulations and algorithmic frameworks.
The survey develops a taxonomy of graph distribution scenarios and characterizes associated statistical heterogeneities.
The opportunities and challenges of learning on graph-structured data in collaborative settings remain largely underexplored.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.