Non-Backtracking Spectrum of Degree-Corrected Stochastic Block Models
Lennart Gulikers, Marc Lelarge, Laurent Massouli\'e

TL;DR
This paper characterizes the spectrum of the non-backtracking matrix in degree-corrected stochastic block models, revealing phase transitions for community detection and establishing connections to the graph Riemann hypothesis.
Contribution
It provides a spectral analysis of the non-backtracking matrix in degree-corrected models, identifying conditions for successful community detection and linking to quasi-Ramanujan properties.
Findings
Leading eigenvalue asymptotic to (a+b)/2 * Φ^{(2)}
Second eigenvalue asymptotic to (a-b)/2 * Φ^{(2)} when conditions hold
Remaining eigenvalues bounded by √ρ
Abstract
Motivated by community detection, we characterise the spectrum of the non-backtracking matrix in the Degree-Corrected Stochastic Block Model. Specifically, we consider a random graph on vertices partitioned into two equal-sized clusters. The vertices have i.i.d. weights with second moment . The intra-cluster connection probability for vertices and is and the inter-cluster connection probability is . We show that with high probability, the following holds: The leading eigenvalue of the non-backtracking matrix is asymptotic to . The second eigenvalue is asymptotic to when , but asymptotically bounded by when . All the remaining eigenvalues are asymptotically…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
