Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation

TL;DR

This paper introduces an online EM algorithm for joint inference and clustering of streaming graph signals modeled as a mixture with non-white excitation, addressing practical challenges in graph learning.

Contribution

It develops a novel online EM method for mixture graph signals with non-white excitation, incorporating a low-rank plus sparse prior for node centrality inference.

Findings

The online EM algorithm converges to a stationary point of the MAP problem.

Numerical experiments validate the effectiveness of the proposed method.

The approach can detect abnormal graph signal generation.

Abstract

This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study a mixture model of filtered low pass graph signals with possibly non-white and low-rank excitation. While the mixture model is motivated from practical scenarios, it presents significant challenges to prior graph learning methods. As a remedy, we consider an inference problem focusing on the node centrality of graphs. We design an expectation-maximization (EM) algorithm with a unique low-rank plus sparse prior derived from low pass signal property. We propose a novel online EM algorithm for inference from streaming data. As an example, we extend the online algorithm to detect if the signals are generated from an abnormal graph. We show that the proposed algorithms converge to a stationary point of the maximum-a-posterior (MAP) problem. Numerical experiments support our analysis.