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New k-NN Classifier Leverages Gromov-Wasserstein Distances for Graphs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a $k$-nearest neighbors ($k$-NN) classification method utilizing Gromov--Wasserstein (GW) and fused Gromov--Wasserstein (fGW) distances. This approach allows for direct comparison of graphs with varying numbers of nodes and can incorporate node features. The study proves the universal consistency of these GW-based $k$-NN classifiers for both general graphs and node-attributed graphs, with experimental results showing strong performance. AI

RANK_REASON This is a research paper detailing a new methodology for graph comparison and classification.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New k-NN Classifier Leverages Gromov-Wasserstein Distances for Graphs

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Kaitlyn Hohmeier, Nicolas Fraiman, Caroline Moosmueller ·

    $k$-Nearest Neighbors in Gromov--Wasserstein Space

    arXiv:2606.10295v1 Announce Type: new Abstract: The Gromov--Wasserstein (GW) distance provides a framework for comparing metric measure spaces, regardless of their underlying structure or geometry. For network-based data, it enables direct comparisons of graphs with different num…

  2. arXiv stat.ML TIER_1 English(EN) · Caroline Moosmueller ·

    $k$-Nearest Neighbors in Gromov--Wasserstein Space

    The Gromov--Wasserstein (GW) distance provides a framework for comparing metric measure spaces, regardless of their underlying structure or geometry. For network-based data, it enables direct comparisons of graphs with different numbers of nodes, without requiring an embedding or…

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