Explore local graph clustering techniques for handling noisy labels in this 20-minute talk by Artur Back de Luca from the University of Waterloo. Gain insights into advanced methods for analyzing complex networks and addressing challenges posed by imperfect data labeling. Learn about cutting-edge approaches to improve clustering accuracy and efficiency in the presence of label noise, applicable to various fields such as social network analysis, bioinformatics, and machine learning.
Overview
Syllabus
Local Graph Clustering with Noisy Labels
Taught by
Fields Institute
Related Courses
-
Market Graph Clustering via QUBO and Digital Annealing
-
Hierarchical Clustering on Asymmetric Networks
-
Graph Constructions for Machine Learning Applications - New Insights and Algorithms
-
Graph Reconstruction via Discrete Morse Theory
-
Building an Interactive Network Graph to Understand Communities
-
Theory of Graph Neural Networks: Representation and Learning
