# Tree Decomposition

3 papers with code • 0 benchmarks • 0 datasets

**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.

## Benchmarks

These leaderboards are used to track progress in Tree Decomposition
## Most implemented papers

# Constraint-based Causal Structure Learning with Consistent Separating Sets

It is achieved by repeating the constraint-based causal structure learning scheme, iteratively, while searching for separating sets that are consistent with the graph obtained at the previous iteration.

# DPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees

We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form.

# Tree Decomposed Graph Neural Network

Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks.