Hierarchical cutting of complex networks reveals insights into balance and chaining effects.
Analyzing the impact of (ω1, ω2) on temporal network properties.
Machine learning algorithms can accurately reconstruct the evolution history of networked complex systems, revealing key co-evolution features and facilitating structure prediction.
Efficiently learning edge weights in networks using directional sign patterns is crucial for understanding complex interactions.
Effective maximum-likelihood estimation for identifying the source of contagion in networks.
The ultrametric backbone is the union of all minimum spanning forests, providing a new generalization of minimum spanning trees to directed graphs.
Keeping signs of edges in network construction improves accuracy for clustering and estimation.
Large language models exhibit social network principles, impacting network formation dynamics.
The authors present two overlapping network community detection algorithms based on extended modularity and cosine functions, applicable to both undirected and directed graphs.
The authors explore the gradual cutting of complex networks through random walks, establishing a hierarchy. They focus on the balance and sizes of components and the permanence of each component.