Active learning: update the model as we learn more
14
The double life of Belief Propagation
15
A phase transition: detectable to undetectable communities
16
Phase transitions in semisupervised learning
17
Hierarchical clustering
18
Clustering nodes with eigenvalues
19
When does this work?
20
The non-backtracking operator
21
Comparing with standard spectral methods
22
Non-backtracking for trust and centrality: avoid the echo chamber
23
Morals
24
Physics culture meets machine learning
25
Challenges
26
Shameless Plug
Description:
Explore a physics-inspired approach to community detection in this 48-minute lecture from the Santa Fe Institute's Annual Science Board Symposium. Delve into the stochastic block model, statistical inference, and belief propagation techniques. Examine phase transitions in community detection, from detectable to undetectable, and investigate hierarchical clustering methods. Learn about the non-backtracking operator and its applications in trust and centrality analysis. Gain insights into the intersection of physics culture and machine learning, and discover the challenges and opportunities in this field.
Physics-Inspired Algorithms and Phase Transitions in Community Detection - 2014 Symposium