Existing works often rely on index structures that store pre-computed transitive relations to achieve efficient graph matching. In this paper, we present a family of stack-based algorithms to handle ...

Existing random graph models introduce unwanted features such as multiple edges and directed cycles when randomizing directed acyclic networks. This paper proposes a new random graph model for ...

The adoption of user-centered ergonomics—a design approach that takes human needs and limitations as its starting point—has guided the creation of components that respond adaptively to the ...

In this tutorial, we demonstrate how to construct an automated Knowledge Graph (KG) pipeline using LangGraph and NetworkX. The pipeline simulates a sequence of intelligent agents that collaboratively ...

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

Fast: The core graph construction is implemented in C++ with a low overhead interface to Python. Every API method supports simple and efficient parallelization through an executor parameter. C ...

Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200031, China, Department of Pharmaceutical Sciences, School of Pharmacy, Bouvé College of Health Sciences, Northeastern ...