q-Exponential random graphs under simple constraints exhibit sparse-dense phase transitions and triadic closure not present in standard ERGs.
Graphs are maximally expressive for higher-order interactions
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
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.
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.
Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.
citing papers explorer
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.