Abstract: Outlier detection is an effective technique for identifying abnormal samples in complex data. Random walks effectively detect outliers by analyzing graph transition patterns. However, ...

nyc-geo-toolkit packages canonical NYC boundary layers and the small helper API needed to discover, normalize, load, subset, and convert them. The initial release focuses on: packaged boundary layers ...

This repository is an example accompanying the DES RAP Book — an open educational resource on reproducible discrete-event simulation (DES) in Python and R. The book demonstrates best practices for ...

Abstract: Random walk centrality is a fundamental metric in graph mining for quantifying node importance and influence, defined as the weighted average of hitting times to a node from all other nodes.

A physics-based Python simulation exploring collision behavior in an AstroBlaster system, focusing on momentum transfer, impact modeling, and numerical computation techniques. #PythonPhysics ...

A computational physics approach to modeling rigid object motion using spring forces in Python. This focuses on how spring systems can approximate real-world rigid body behavior through numerical ...