3260 papers • 126 benchmarks • 313 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.
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A simple modi-cation of PC and PC-derived algorithms is proposed so as to ensure that all separating sets identified to remove dispensable edges are consistent with the final graph, enhancing the explainability of constraint-based methods.
This work proposes a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form, and shows that this framework is competitive with the state-of-the-art exact weighted model counters Cachet, c2d, d4, and miniC2D.
This work theoretically analyzes the feature smoothing between neighborhoods in different layers and empirically demonstrates the variance of the homophily level across neighborhoods at different layers, and proposes a tree decomposition method to disentangle Neighborhood Decomposed Graph Neural Network (TDGNN), which can flexibly incorporate information from large receptive fields and aggregate this information utilizing the multi-hop dependency.
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