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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A universal second-order reduction maps delayed Kuramoto-Daido networks to inertia-equipped rotator networks with triadic interactions, accurately predicting splay, cyclops, and chimera states across topologies and heterogeneities.
Memory-biased random walks drive network avalanches where dissipative stress transfer stabilizes broad finite cascades on Watts-Strogatz graphs while fixed transfer produces runaways, and shuffled controls show dissipation dominates memory ordering.
citing papers explorer
-
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.