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TopoX: A Suite of Python Packages for Machine Learning on Topological Domains

2024-02-04 · Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane

We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelX is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at https://pyt-team.github.io/}{https://pyt-team.github.io/.

📄 PDF Abstract BibTeX arXiv:2402.02441

Code (1)

pyt-team/toponetx 공식 구현

ICML 2023 Topological Deep Learning Challenge : Design and Results

2023-09-26 · Mathilde Papillon, Mustafa Hajij, Helen Jenne, Johan Mathe 외

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-…

Deep Learning

An Empirical Study of Vulnerabilities in Python Packages and Their Detection

2025-09-04 · Haowei Quan, Junjie Wang, Xinzhe Li, Terry Yue Zhuo 외 arxiv

In the rapidly evolving software development landscape, Python stands out for its simplicity, versatility, and extensive ecosystem. Python packages, as units of organization, reusability, and distribution, have become a …

Vulnerability Detection

Efficient Solvers for SLOPE in R, Python, Julia, and C++

2025-11-04 · Johan Larsson, Malgorzata Bogdan, Krystyna Grzesiak, Mathurin Massias 외 arxiv

We present a suite of packages in R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. The packages feature a highly efficient hybrid coordinate descent algorithm that f…

HiPart: Hierarchical Divisive Clustering Toolbox

2022-09-18 · Panagiotis Anagnostou, Sotiris Tasoulis, Vassilis Plagianakos, Dimitris Tasoulis

This paper presents the HiPart package, an open-source native python library that provides efficient and interpret-able implementations of divisive hierarchical clustering algorithms. HiPart supports interactive visualiz…

Clustering Computational Efficiency

Rasa: Open Source Language Understanding and Dialogue Management

2017-12-14 · Tom Bocklisch, Joey Faulkner, Nick Pawlowski, Alan Nichol

We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software. Their purpose is to make machine-learning based dialogue management and language understa…

BIG-bench Machine Learning Dialogue Management Management Philosophy