Error-tolerant graph matching using node contraction
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Summary
This paper presents an approach to error-tolerant graph matching using node contraction where the given graph is transformed into another graph by contracting smaller degree nodes, which can be used as a trade-off between execution time and accuracy requirements of various graph matching applications.
- Type
- article
- Published
- 2018-12-01
- Cited by
- 13
- References
- 41
- OpenAlex
- https://openalex.org/W2891378728
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:56177922
Keywords
Line graph, Factor-critical graph, Butterfly graph, Computer science, Null graph
References
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- Inexact graph matching using genetic search
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- Improving bipartite graph edit distance approximation using various search strategies
- Estimating Graph Edit Distance Using Lower and Upper Bounds of Bipartite Approximations
- The Representation and Matching of Pictorial Structures
- Combining Evidence in Probabilistic Relaxation
- Diffusion Kernels on Statistical Manifolds
- Structural Matching by Discrete Relaxation
- Indexing hierarchical structures using graph spectra
- Shape recognition from large image libraries by inexact graph matching
- Thirty Years Of Graph Matching In Pattern Recognition
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- Mining clique frequent approximate subgraphs from multi-graph collections
- Vulnerability Identification and Evaluation of Interdependent Natural Gas-Electricity Systems
- Error-tolerant approximate graph matching utilizing node centrality information
- Multilevel Graph Matching Networks for Deep Graph Similarity Learning
- Fault-Tolerant Routing With Load Balancing in LeTQ Networks
- Building Multiple Classifier Systems Using Linear Combinations of Reduced Graphs
- Similarity Measurement for Graph Data: An Improved Centrality and Geometric Perspective-Based Approach
- A Graph Neural Network-Based Approach With Dynamic Multiqueue Optimization Scheduling (DMQOS) for Efficient Fault Tolerance and Load Balancing in Cloud Computing
- Tri-level interaction fusion network for graph similarity learning
- Some Algorithms on Exact, Approximate and Error-Tolerant Graph Matching
- An Algorithm for Mining Frequent Approximate Subgraphs with Structural and Label Variations in Graph Collections
- Inexact Graph Matching Using Centrality Measures
- Exploiting bidirectional Mamba interaction for graph similarity learning
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