Cycle-density filtrations based on motif densities enable persistent homology to distinguish non-isomorphic graphs nearly perfectly and achieve strong performance on real-world graph property prediction.
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Chordless cycle filtrations from topological data analysis combined with a neural network trained on synthetic graphs provide a data-driven estimate of the dimensionality of hyperbolic geometry in complex networks.
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Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction
Cycle-density filtrations based on motif densities enable persistent homology to distinguish non-isomorphic graphs nearly perfectly and achieve strong performance on real-world graph property prediction.
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Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis
Chordless cycle filtrations from topological data analysis combined with a neural network trained on synthetic graphs provide a data-driven estimate of the dimensionality of hyperbolic geometry in complex networks.