Learning Expanding Graphs for Signal Interpolation

TL;DR

This paper introduces a data-driven method for signal interpolation on expanding graphs, addressing the challenge of unknown connectivity for incoming nodes in dynamic graph scenarios.

Contribution

It proposes a stochastic attachment model and an alternating descent algorithm to estimate connectivity parameters for signal interpolation on growing graphs.

Findings

Effective in cold start recommendation scenarios

Handles non-convexity through marginal convexification

Validated with synthetic and real data

Abstract

Performing signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectivity is typically unknown; hence, making more challenging the downstream tasks in applications like cold start recommendation. We address such a challenge for signal interpolation at the incoming nodes blind to the topological connectivity of the specific node. Specifically, we propose a stochastic attachment model for incoming nodes parameterized by the attachment probabilities and edge weights. We estimate these parameters in a data-driven fashion by relying only on the attachment behaviour of earlier incoming nodes with the goal of interpolating the signal value. We study the non-convexity of the problem at hand, derive conditions when it can be marginally convexified, and propose an alternating projected descent approach between estimating the attachment probabilities and the edge weights. Numerical experiments with synthetic and real data dealing in cold start collaborative filtering corroborate our findings.