Online Network Inference from Graph-Stationary Signals with Hidden Nodes

TL;DR

This paper introduces an online method for inferring graph structures from streaming data with hidden nodes, using stationary signals and convex optimization, enabling real-time graph learning despite incomplete observations.

Contribution

It proposes a novel convex optimization framework and proximal gradient algorithm for online graph inference with hidden nodes and streaming incomplete data.

Findings

Effective in real-time graph learning with missing data

Theoretical conditions for online and batch solution similarity

Validated on synthetic and real-world datasets

Abstract

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations.