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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
HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks—0
Graph Inference with Effective Resistance Queries—0
A Hybrid Technique for Plant Disease Identification and Localisation in Real-time—0
Bounds on the Treewidth of Level-k Rooted Phylogenetic Networks—0
Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning—0
Lifting uniform learners via distributional decomposition—0
Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs—0
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs—0
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Computing the Newton-step faster than Hessian accumulation—0
TD-GEN: Graph Generation With Tree Decomposition—0
A Framework For Differentiable Discovery Of Graph Algorithms—0
Learning Elimination Ordering for Tree Decomposition Problem—0
A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition—0
DPMC: Weighted Model Counting by Dynamic Programming on Project-Join TreesCode0
Constraint-based Causal Structure Learning with Consistent Separating SetsCode0
Mixtape: Breaking the Softmax Bottleneck Efficiently—0
Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics—0
Ordered Tree Decomposition for HRG Rule Extraction—0
Sparse residual tree and forest—0
Optimizing Answer Set Computation via Heuristic-Based Decomposition—0
Image Segmentation Based on Multiscale Fast Spectral Clustering—0
Cache Transition Systems for Graph Parsing—0
Bayesian Learning of Clique Tree Structure—0
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