Dimosthenis Pasadakis
I am a postdoctoral fellow at Università della Svizzera italiana (USI) in Lugano, Switzerland. The focus of my research is centered around algorithms for graph learning and combinatorial optimization for graph clustering and anomaly detection. Currently, I am leading the project “Directed acyclic graph partitioning for scheduling tasks“, financed by the Huawei Research Center Zürich. I have an MSc and PhD in Computational Science from USI, and a Degree of Physics from the Aristotle University of Thessaloniki, Greece.
Personal website: https://dmspas.github.io/
Intervention
We present GraphLab.jl, a Julia package designed to facilitate the study, experimentation, and research of graph partitioning. GraphLab.jl provides a framework for exploring the principles and trade-offs of partitioning algorithms through hands-on tools. It implements a growing set of methods—including coordinate, inertial, and spectral bisection, random spheres, space-filling curves, and nested dissection—with support for recursive partitioning. The package includes routines for generating adjacency matrices, computing partition quality metrics, benchmarking problems, and visualizing partitioned graphs. GraphLab.jl enables integration with external graph partitioning software, thus allowing users to compare additional methods and results in a unified environment. Our work aims to introduce Julia's capabilities to learners and researchers engaging in graph theory and related partitioning problems.