Learning Partial Graph Matching via Optimal Partial Transport

TL;DR

This paper introduces a new optimization framework for partial graph matching based on optimal partial transport, enabling efficient and exact solutions for complex matching scenarios with unmatched nodes.

Contribution

It proposes a novel optimization objective, connects partial graph matching to linear sum assignment, and develops an end-to-end deep learning architecture with a new partial matching loss.

Findings

Efficient cubic time complexity solutions for partial graph matching.

Superior performance on standard benchmarks.

Effective handling of unmatched nodes in graph matching.

Abstract

Partial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for partial graph matching, a significant limitation remains: the absence of an optimization objective that fully captures the problem's intrinsic nature while enabling efficient solutions. In this paper, we propose a novel optimization framework for partial graph matching, inspired by optimal partial transport. Our approach formulates an objective that enables partial assignments while incorporating matching biases, using weighted total variation as the divergence function to guarantee optimal partial assignments. Our method can achieve efficient, exact solutions within cubic worst case time complexity. Our contributions are threefold: (i) we introduce a novel optimization objective that balances matched and unmatched nodes; (ii) we establish a connection between partial graph matching and linear sum assignment problem, enabling efficient solutions; (iii) we propose a deep graph matching architecture with a novel partial matching loss, providing an end-to-end solution. The empirical evaluations on standard graph matching benchmarks demonstrate the efficacy of the proposed approach.