推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.

KaHIP -- HIGH Quality Partitioning.

Mt-KaHyPar (Multi-Threaded Karlsruhe Hypergraph Partitioner) is a shared-memory multilevel graph and hypergraph partitioner equipped with parallel implementations of techniques used in the best sequential partitioning algorithms. Mt-KaHyPar can partition extremely large hypergraphs very fast and with high quality.

Exercises for the Algorithm Engineering (ALE) course at University of Pisa

Karlsruhe Rapid Ridesharing (KaRRi) Dynamic Taxi Sharing Dispatcher.

A list of all publications related to the KaHyPar frameworks.

Dynamic Matching in Practice — fully dynamic maximal matching algorithms

Material for the AlgLab (Winter 2024/2025) @ TU Braunschweig

Experiment execution and result management for empirical evaluations of algorithms in Python.

StreamCPI is a framework for reducing the memory consumption of streaming graph partitioners by compressing the array of block assignments used by such partitioners with run-length compression.

Buffered Streaming Graph Partitioning

Fully dynamic exact and heuristic algorithms for edge orientation (delta-orientation)

Exact minimum cuts in hypergraphs at scale using FPT kernelization

A scalable Python framework that transforms algorithm practice into a data-driven, testable, and high-performance workflow—built to help developers grow faster and understand algorithms more deeply.

Collection of our hypergraph partitioning experiments

Streaming Process Mapping

Fully dynamic solver for the Maximum (Weight) Independent Set problem

Secretly a Master's Thesis

Graph Coloring implementation based on XRLF algorithm