Learning Combinatorial Embedding Networks for Deep Graph Matching

04/01/2019
by   Runzhong Wang, et al.
0

Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises. To this end, this paper devises an end-to-end differentiable deep network pipeline to learn the affinity for graph matching. It involves a supervised permutation loss regarding with node correspondence to capture the combinatorial nature for graph matching. Meanwhile deep graph embedding models are adopted to parameterize both intra-graph and cross-graph affinity functions, instead of the traditional shallow and simple parametric forms e.g. a Gaussian kernel. The embedding can also effectively capture the higher-order structure beyond second-order edges. The permutation loss model is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network allows for varying numbers of nodes in graphs for training and inference. Moreover, our network is class-agnostic with some generalization capability across different categories. All these features are welcomed for real-world applications. Experiments show its superiority against state-of-the-art graph matching learning methods.

READ FULL TEXT

page 6

page 8

research
11/26/2019

Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching

Graph matching involves combinatorial optimization based on edge-to-edge...
research
12/16/2020

Deep Reinforcement Learning of Graph Matching

Graph matching under node and pairwise constraints has been a building b...
research
02/20/2015

A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting

This paper addresses the problem of matching N weighted graphs referring...
research
10/19/2022

Learning Universe Model for Partial Matching Networks over Multiple Graphs

We consider the general setting for partial matching of two or multiple ...
research
01/05/2023

PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching

Graph matching can be formalized as a combinatorial optimization problem...
research
12/08/2022

Graph Matching with Bi-level Noisy Correspondence

In this paper, we study a novel and widely existing problem in graph mat...
research
01/26/2022

On the Power of Gradual Network Alignment Using Dual-Perception Similarities

Network alignment (NA) is the task of finding the correspondence of node...

Please sign up or login with your details

Forgot password? Click here to reset