Structural synergy, defined as the excess predictive power of a joint model over the best additive model, isolates non-additive interaction mechanisms from dependency-driven statistical synergy in complex systems.
Graphs are maximally expressive for higher-order interactions
8 Pith papers cite this work. Polarity classification is still indexing.
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q-Exponential random graphs under simple constraints exhibit sparse-dense phase transitions and triadic closure not present in standard ERGs.
Moderately disassortative dyadic networks and moderately assortative hypergraphs can be less robust under random node failure than their extreme-correlation counterparts.
Multi-step retweet pathways in Italian COVID-19 Twitter data weaken intra-community attention concentration and produce non-uniform cross-community redistribution with directional asymmetries not explained by size or direct connectivity.
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.
Standard 3-body Kuramoto couplings are mathematically equivalent to pairwise connections through latent transmission channels, and relaxing the adiabatic limit reveals symmetry-dependent bistability and finite-size clustering effects.
Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.
citing papers explorer
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From Statistical to Structural Synergy: A Predictability Framework to Quantify the Effects due to High-Order Mechanisms
Structural synergy, defined as the excess predictive power of a joint model over the best additive model, isolates non-additive interaction mechanisms from dependency-driven statistical synergy in complex systems.
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q-Exponential Random Graphs: higher-order networks from simple constraints
q-Exponential random graphs under simple constraints exhibit sparse-dense phase transitions and triadic closure not present in standard ERGs.
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Nonmonotonic percolation threshold in correlated networks and hypergraphs
Moderately disassortative dyadic networks and moderately assortative hypergraphs can be less robust under random node failure than their extreme-correlation counterparts.
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Beyond Direct Retweets: Multi-Step Pathways in Italian COVID-19 Twitter
Multi-step retweet pathways in Italian COVID-19 Twitter data weaken intra-community attention concentration and produce non-uniform cross-community redistribution with directional asymmetries not explained by size or direct connectivity.
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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.
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Impact of Channel Dynamics on Higher-order Interactions of Oscillators
Standard 3-body Kuramoto couplings are mathematically equivalent to pairwise connections through latent transmission channels, and relaxing the adiabatic limit reveals symmetry-dependent bistability and finite-size clustering effects.
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Topological Signal Processing: An Application-Oriented Tutorial
Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.