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Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs.
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