In both ordinary networks and hypergraphs, the percolation threshold exhibits nonmonotonic dependence on assortativity, with moderately disassortative configurations often the most fragile under random node removal.
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3 Pith papers cite this work. Polarity classification is still indexing.
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GNNs and HOMP models saturate an extended manifold triangulation benchmark when given appropriate representations but show no generalization beyond combinatorial structure, indicating a gap in topology-aware learning.
Higher-order ecological interactions can be accurately reproduced by effective pairwise models fitted to abundance time series, so interaction structure cannot be reliably inferred from time series data alone.
citing papers explorer
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Nonmonotonic percolation threshold in correlated networks and hypergraphs
In both ordinary networks and hypergraphs, the percolation threshold exhibits nonmonotonic dependence on assortativity, with moderately disassortative configurations often the most fragile under random node removal.
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No Triangulation Without Representation: Generalization in Topological Deep Learning
GNNs and HOMP models saturate an extended manifold triangulation benchmark when given appropriate representations but show no generalization beyond combinatorial structure, indicating a gap in topology-aware learning.
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Higher-order interactions in ecology can be hidden in plain sight
Higher-order ecological interactions can be accurately reproduced by effective pairwise models fitted to abundance time series, so interaction structure cannot be reliably inferred from time series data alone.