The art of finding patterns or communities plays a central role in the analysis of structured data such as networks. Community detection in graphs has become a field on its own. Real-world networks, ...

Directed graphs and their afferent/efferent capacities are produced by Markov modeling of the universal cover of undirected graphs simultaneously with the calculation of volume entropy. Using these ...

Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs ...

Advanced Artificial Intelligence Theoretical and Computational Chemistry Laboratory, School of Chemistry, University of Hyderabad, Hyderabad, Telangana 500046, India ...

In this blog post, I will begin by introducing the concept of cut sparsifier for a given graph \(G\), which has been a powerful tool in the design of graph algorithms. Following that, I will present a ...

CS: Data Structures - Directed Graph DFS & BFS --- Use edge node structure for adjacency list (directed graph), custom queue algorithm for BFS, custom list class implementation for adjacency list. BFS ...

In this article, dynamical robustness of a directed complex network with additive noise is inverstigated. The failure of a node in the network is modeled by injecting noise into the node. Under the ...

Title: Have directed acyclic graphs (DAGs) fulfilled their promise in epidemiology and health research? Abstract: Causal directed acyclic graphs (DAGs) are among the most widely used causal diagrams.

Abstract: Cycles and knots in directed graphs are problems that can be associated with deadlocks in database and communication systems. Many algorithms to detect cycles and knots in directed graphs ...