scientific article; zbMATH DE number 7559112
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Publication:5090449
DOI10.4230/LIPIcs.STACS.2019.3MaRDI QIDQ5090449
Publication date: 18 July 2022
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cites Work
- Unnamed Item
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- Analysis of agglomerative clustering
- Graph isomorphism is in the low hierarchy
- Isomorphism of graphs of bounded valence can be tested in polynomial time
- A note on compact graphs
- An optimal lower bound on the number of variables for graph identification
- On maximum common subgraph problems in series-parallel graphs
- The data science design manual
- Sherali-Adams relaxations of graph isomorphism polytopes
- Practical graph isomorphism. II.
- Sherali--Adams Relaxations and Indistinguishability in Counting Logics
- A Polynomial-Time Algorithm for Computing the Maximum Common Subgraph of Outerplanar Graphs of Bounded Degree
- Testing Cluster Structure of Graphs
- Fixed-Parameter Tractable Canonization and Isomorphism Test for Graphs of Bounded Treewidth
- Finding Largest Common Substructures of Molecules in Quadratic Time
- Polynomial algorithms for graph isomorphism and chromatic index on partial k-trees
- PEBBLE GAMES AND LINEAR EQUATIONS
- Measure and conquer
- Random Graph Isomorphism
- Subtree Isomorphism in O(n5/2)
- A note on block-and-bridge preserving maximum common subgraph algorithms for outerplanar graphs
- Lov\'asz Meets Weisfeiler and Leman
- An improved isomorphism test for bounded-tree-width graphs
- Graph Similarity and Approximate Isomorphism
- On the approximability of the maximum common subgraph problem
- Frequent Pattern Mining
- Foundations of Data Science
- Data Mining
- Structure Theorem and Isomorphism Test for Graphs with Excluded Topological Subgraphs
- Descriptive Complexity, Canonisation, and Definable Graph Structure Theory
- Graph isomorphism in quasipolynomial time [extended abstract]
- Understanding Machine Learning
- Algorithm 457: finding all cliques of an undirected graph
- Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering
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