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Tree Decomposition

Tree Decomposition is a technique in graph theory and computer science for representing a graph as a tree, where each node in the tree represents a set of vertices in the original graph. The goal of tree decomposition is to divide the graph into smaller, more manageable pieces, and to use the tree to represent the relationships between these pieces.

Papers

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Showing 1–25 of 34 papers

TitleStatusHype
Tree Decomposed Graph Neural NetworkCode1
Constraint-based Causal Structure Learning with Consistent Separating SetsCode0
DPMC: Weighted Model Counting by Dynamic Programming on Project-Join TreesCode0
A Hybrid Technique for Plant Disease Identification and Localisation in Real-time—0
A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition—0
Bayesian Learning of Clique Tree Structure—0
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference—0
Bounds on the Treewidth of Level-k Rooted Phylogenetic Networks—0
Cache Transition Systems for Graph Parsing—0
Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs—0
Clustering Tree-structured Data on Manifold—0
Computing the Newton-step faster than Hessian accumulation—0
Density Propagation and Improved Bounds on the Partition Function—0
Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics—0
Graph Inference with Effective Resistance Queries—0
Sparse residual tree and forest—0
Image Analysis Using a Dual-Tree M-Band Wavelet Transform—0
Image Segmentation Based on Multiscale Fast Spectral Clustering—0
Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning—0
Inference in Sparse Graphs with Pairwise Measurements and Side Information—0
iTreePack: Protein Complex Side-Chain Packing by Dual Decomposition—0
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Learning Elimination Ordering for Tree Decomposition Problem—0
Lifting uniform learners via distributional decomposition—0
Minimum Error Tree Decomposition—0
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