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
Benchmarks
Datasets
Greatest papers with code
graph2vec: Learning Distributed Representations of Graphs
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
Ranked #1 on
Malware Detection
on Android Malware Dataset
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.
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.
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
• martius-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.
Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
• mys007/ecc
•
A graph is a powerful concept for representation of relations between pairs of entities.
MolGAN: An implicit generative model for small molecular graphs
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.
Ranked #3 on
Molecular Graph Generation
on InterBioScreen
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)
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences
In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences
In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.
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.