CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment

TL;DR

CAPER is a multilevel framework that enhances network alignment accuracy and speed by coarsening, aligning, projecting, and refining graphs across multiple resolutions, improving existing methods significantly.

Contribution

Introduces CAPER, a novel multilevel framework that enforces alignment consistency across graph resolutions and accelerates existing network alignment algorithms.

Findings

Improves alignment accuracy by an average of 33%.

Reduces runtime by an order of magnitude.

Enhances diverse existing methods with minimal additional cost.

Abstract

Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel alignment framework that Coarsens the input graphs, Aligns the coarsened graphs, Projects the alignment solution to finer levels and Refines the alignment solution. We show that CAPER can improve upon many different existing network alignment algorithms by enforcing alignment consistency across multiple graph resolutions: nodes matched at finer levels should also be matched at coarser levels. CAPER also accelerates the use of slower network alignment methods, at the modest cost of linear-time coarsening and refinement steps, by allowing them to be run on smaller coarsened versions of the input graphs. Experiments show that CAPER can improve upon diverse network alignment methods by an average of 33% in accuracy and/or an order of magnitude faster in runtime.