Graph Matching

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

11 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 163 papers with code (477 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• MolGAN: An implicit generative model for small molecular graphs
  30 May 2018 11 repositories listed Syntology ran 3 of 17 samples · 14 unverified · 1 pointer-only (licence)
  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…
• graph2vec: Learning Distributed Representations of Graphs
  17 Jul 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
• Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
  25 Mar 2020 5 repositories listed Syntology ran 5 of 11 samples · 6 unverified · 3 pointer-only (licence)
  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.
• GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
  20 Sep 2023 4 repositories listed
  This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains.
• Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
  2 Apr 2021 4 repositories listed
  MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e.
• PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
  1 Oct 2023 3 repositories listed Syntology ran 8 of 8 samples · 0 unverified
  Different pre-screening methods have been developed for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction…
• Graph Matching with Bi-level Noisy Correspondence
  8 Dec 2022 3 repositories listed Syntology ran 7 of 7 samples · 0 unverified · 7 pointer-only (licence)
  In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence…
• Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
  19 Oct 2020 3 repositories listed
  The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).
• AMR Similarity Metrics from Principles
  29 Jan 2020 3 repositories listed
  Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs.
• Dynamic Graph Representation for Partially Occluded Biometrics
  1 Dec 2019 3 repositories listed
  During dynamic graph matching, we propose a novel strategy to measure the distances of both nodes and adjacent matrixes.
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
  29 Apr 2019 3 repositories listed
  This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions.
• Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions
  27 Jul 2023 2 repositories listed
  Occlusion is a common problem with biometric recognition in the wild.
• Unsupervised Manifold Alignment with Joint Multidimensional Scaling
  6 Jul 2022 2 repositories listed Syntology ran 14 of 17 samples · 3 unverified · 16 pointer-only (licence)
  Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between…
• A Comparative Study of Graph Matching Algorithms in Computer Vision
  1 Jul 2022 2 repositories listed Syntology ran 1 of 22 samples · 21 unverified
  To address these shortcomings, we present a comparative study of graph matching algorithms.
• Backpropagation through Combinatorial Algorithms: Identity with Projection Works
  30 May 2022 2 repositories listed
  Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities.
• Graph Neural Networks for Cross-Camera Data Association
  17 Jan 2022 2 repositories listed
  To avoid the usage of fixed distances, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity.
• Joint Graph Learning and Matching for Semantic Feature Correspondence
  1 Sep 2021 2 repositories listed
  In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.
• Adaptive Edge Attention for Graph Matching with Outliers
  19 Aug 2021 2 repositories listed
  To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
• Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
  1 Aug 2021 2 repositories listed
  Graph matching is an important problem that has received widespread attention, especially in the field of computer vision.
• Robust Point Cloud Registration Framework Based on Deep Graph Matching
  7 Mar 2021 2 repositories listed Syntology ran 25 of 38 samples · 13 unverified · 38 pointer-only (licence)
  In this paper, we propose a novel deep graph matchingbased framework for point cloud registration.
• Fusion Moves for Graph Matching
  28 Jan 2021 2 repositories listed
  We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching.
• Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
  16 Dec 2020 2 repositories listed Syntology ran 7 of 8 samples · 1 unverified · 8 pointer-only (licence)
  As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i.
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
  18 Mar 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  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…
• Deep Graph Matching Consensus
  27 Jan 2020 2 repositories listed
  This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
• Learning Cross-modal Context Graph for Visual Grounding
  20 Nov 2019 2 repositories listed
  To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the…
• MTab: Matching Tabular Data to Knowledge Graph using Probability Models
  1 Oct 2019 2 repositories listed
  This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019).
• IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
  22 Jul 2019 2 repositories listed
  However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property.
• SemBleu: A Robust Metric for AMR Parsing Evaluation
  26 May 2019 2 repositories listed
  Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs.
• SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
  10 Apr 2019 2 repositories listed
  Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors.
• Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  17 Jan 2019 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.