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Everything about Graph Coarsening Algorithm
A Graph Coarsening Algorithm is a family of metaheuristic algorithms used to reduce the size and complexity of a large graph while preserving its key structural properties. These algorithms form the core of multilevel frameworks, which transform complex optimization problems on massive graphs into smaller, more manageable ones. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Graph Coarsening AlgorithmA 5-minute orientation
- Skim a recent paper: Net Clusting Based Low Complexity Coarsening Algorithm In k-way Hypergraph PartitioningInfluential work since 2022
- Pick up a classic book: Computer Simulation Studies in Condensed-Matter Physics XVIThe most-reprinted book on the subject
- Read the landmark paper: Heuristic Algorithm for Approximation Betweenness Centrality Using Graph CoarseningCited 8 times
- Go sideways: Graph partitionA closely related topic
- Go sideways: Graph neural networkA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Heuristic Algorithm for Approximation Betweenness Centrality Using Graph Coarsening
Compatible coarsening in the multigraph algorithm
An efficient loop and clique coarsening algorithm for graph classification
Pasco (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms
🆕 Influential since 2022
📚 Books worth reading
Computer Simulation Studies in Condensed-Matter Physics XVIDavid P. Landau · 2004
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- Graph cuts in computer vision and artificial intelligence As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems…
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- Consensus clustering Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles or…