Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability

TL;DR

This paper introduces JOFC, a new approximate graph matching algorithm that uses seeded data and joint optimization to embed graphs into a common space for improved matching across diverse graph types.

Contribution

The paper proposes a novel JOFC algorithm that effectively incorporates seeded data and jointly optimizes fidelity and commensurability for graph matching.

Findings

Demonstrates versatility across various graph characteristics

Effective in weighted, directed, and looped graphs

Handles many-to-one, many-to-many, and soft seedings

Abstract

We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated data examples, we demonstrate the versatility of our algorithm in matching graphs with various characteristics--weightedness, directedness, loopiness, many-to-one and many-to-many matchings, and soft seedings.