About

Graph Matching is the problem of finding correspondences between two sets of vertices while preserving complex relational information among them. Since the graph structure has a strong capacity to represent objects and robustness to severe deformation and outliers, it is frequently adopted to formulate various correspondence problems in the field of computer vision. Theoretically, the Graph Matching problem can be solved by exhaustively searching the entire solution space. However, this approach is infeasible in practice because the solution space expands exponentially as the size of input data increases. For that reason, previous studies have attempted to solve the problem by using various approximation techniques.

Source: Consistent Multiple Graph Matching with Multi-layer Random Walks Synchronization

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Datasets

Greatest papers with code

graph2vec: Learning Distributed Representations of Graphs

17 Jul 2017benedekrozemberczki/karateclub

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MATCHING

Learning Combinatorial Embedding Networks for Deep Graph Matching

ICCV 2019 Thinklab-SJTU/PCA-GM

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.

GRAPH EMBEDDING GRAPH MATCHING

High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

CVPR 2020 wangguanan/light-reid

When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.

GRAPH MATCHING PERSON RE-IDENTIFICATION

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

25 Mar 2020martius-lab/blackbox-backprop

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.

COMBINATORIAL OPTIMIZATION GRAPH MATCHING

Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back

24 Jan 2019mys007/ecc

A graph is a powerful concept for representation of relations between pairs of entities.

GRAPH MATCHING

MolGAN: An implicit generative model for small molecular graphs

30 May 2018nicola-decao/MolGAN

Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures.

GRAPH MATCHING

Deep Graph Matching Consensus

ICLR 2020 rusty1s/deep-graph-matching-consensus

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.

Ranked #2 on Entity Alignment on DBP15k zh-en (using extra training data)

ENTITY ALIGNMENT GRAPH MATCHING KNOWLEDGE GRAPHS

SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences

CVPR 2019 intellhave/SDRSAC

In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.

GRAPH MATCHING POINT CLOUD REGISTRATION

SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences

6 Apr 2019intellhave/SDRSAC

In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.

GRAPH MATCHING POINT CLOUD REGISTRATION

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

ACL 2019 syxu828/Crosslingula-KG-Matching

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs.

ENTITY EMBEDDINGS GRAPH MATCHING