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Training Free Graph Neural Networks for Graph Matching

arXiv 2201.05349 ↗First posted 14 Jan 2022cs.LGcited by 0

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Abstract (arXiv)

We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free). TFGM provides four widely applicable principles for designing training-free GNNs and is generalizable to supervised, semi-supervised, and unsupervised graph matching. The keys are to handcraft the matching priors, which used to be learned by training, into GNN's architecture and discard the components inessential under the training-free setting. Further analysis shows that TFGM is a linear relaxation to the quadratic assignment formulation of graph matching and generalizes TFGM to a broad set of GNNs. Extensive experiments show that GNNs with TFGM achieve comparable (if not better) performances to their fully trained counterparts, and demonstrate TFGM's superiority in the unsupervised setting. Our code is available at https://github.com/acharkq/Training-Free-Graph-Matching.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 43 of its 84 references that are in the map. First posted on arXiv 14 Jan 2022.

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References · 43

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  8. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  9. 2016Matching Networks for One Shot Learningcited by 2,246
  10. 2019Simplifying Graph Convolutional Networkscited by 1,470
  11. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  12. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
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  1. 2017Deep Image Priorcited by 1,097
  2. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
  3. 2020Principal Neighbourhood Aggregation for Graph Netscited by 477
  4. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  5. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  6. 2019Revisiting Graph Neural Networks: All We Have is Low-Pass Filterscited by 260
  7. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  8. 2018Neighbourhood Consensus Networkscited by 173
  9. 2017SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernelscited by 160
  10. 2016Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignmentcited by 128
  11. 2017Cross-lingual Entity Alignment via Joint Attribute-Preserving Embeddingcited by 126
  12. 2020A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphscited by 119
  13. 2019Learning Combinatorial Embedding Networks for Deep Graph Matchingcited by 98
  14. 2020Graph Structured Network for Image-Text Matchingcited by 93
  15. 2020Deep Graph Matching Consensuscited by 85
  16. 2019Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matchingcited by 83
  17. 2016Universal Correspondence Networkcited by 82
  18. 2020Deep Graph Matching via Blackbox Differentiation of Combinatorial Solverscited by 81
  19. 2019Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Networkcited by 68
  20. 2020Exploring and Evaluating Attributes, Values, and Structures for Entity Alignmentcited by 63
  21. 2017Detect-and-Track: Efficient Pose Estimation in Videoscited by 51
  22. 2020Visual Pivoting for (Unsupervised) Entity Alignmentcited by 46
  23. 2020Neighborhood Matching Network for Entity Alignmentcited by 44
  24. 2019Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classificationcited by 39
  25. 2021Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Miningcited by 37
  26. 2020On Graph Neural Networks versus Graph-Augmented MLPscited by 30
  27. 2016A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matchingcited by 26
  28. 2020Image-to-Image Retrieval by Learning Similarity between Scene Graphscited by 23
  29. 2020Deep Graph Matching and Searching for Semantic Code Retrievalcited by 20
  30. 2019KerGM: Kernelized Graph Matchingcited by 13
  31. 2020Active Learning for Entity Alignmentcited by 5

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