paperdirected-graphhermitian-adjacencyspectral-clusteringaistats-2020flow-imbalance

Cucuringu, Li, Sun & Zanetti (AISTATS 2020) — Hermitian Matrices for Clustering Directed Graphs

Authors: Mihai Cucuringu (Oxford / Alan Turing Institute), He Li (Oxford / Alan Turing), He Sun (Edinburgh), Luca Zanetti (Oxford / Alan Turing).

Venue: AISTATS 2020 (PMLR 108).

Contribution

Introduces the Hermitian adjacency approach to clustering directed graphs: \tilde A = i(A - A^\top) which is skew-symmetric (i \cdot anti-symmetric matrix) and thus Hermitian with real eigenvalues. Take the top k (or \lceil k/2 \rceil) eigenvectors of \tilde A, project onto \mathbb{C}^p, k-means.

The algorithm directly optimizes a pairwise flow imbalance objective \sum_{a < b} |\text{Cut}(C_a, C_b) - \text{Cut}(C_b, C_a)| — i.e., clustering such that between any two clusters, the edge flow is strongly asymmetric.

Theoretical guarantee: in the Directed Stochastic Block Model (DSBM) setting, the algorithm correctly recovers clusters under suitable signal-to-noise conditions.

Importance to this wiki

This is the canonical algorithm in the Hermitian clustering family:

Paper Math Objective Time Guarantee
Cucuringu 2020 (this paper) i(A-A^\top) Hermitian adjacency pairwise flow imbalance O(kp^2) DSBM
Laenen-Sun 2020 (NeurIPS) Hermitian Laplacian with phase factor chain flow ratio \tilde O(m) general digraphs
Bennett 2022 (ML) Hermitian adjacency + RW normalization pairwise flow imbalance O(p^2TL + kp^2) empirical (US equity)

entities/He Sun is the common methodological thread across Cucuringu 2020 and Laenen-Sun 2020. entities/Mihai Cucuringu is the common author between Cucuringu 2020, Bennett 2022 (lead-lag application), and SPONGE.

See also