Mastodawn

Makes my day🙂 Graph ML identifies promising additional drug indications for diseases such as acute lymphoblastic leukemia and Alzheimer’s disease.
https://www.nature.com/articles/s41551-025-01598-z
#KnowledgeGraphs #GraphML #DrugRepurposing

DST Global Jan 7

LOGOS-κ: Новый язык программирования для моделирования сложных систем

6 января 2026 года Российская компания DST Global и проект Λ-Универсум представили LOGOS-κ — не просто новый язык программирования...

#LOGOS-κ #LOGOSκ #языкпрограммирования #Логос #GraphML #NIGC #DomainSpecificLanguage #DSL #Lambda #Omega #Универсум #Universum #ΛУниверсум #AUniversum #АУниверсум #Искусственныйинтеллект

Ссылки:
Репозиторий: https://github.com/A-Universum/logos-k
Исходный манифест Λ-Универсума: https://github.com/a-universum

Orbifold Consulting Nov 13

Node and graph embeddings are a bit of a burden if you want to do simple experiments: PyG (aka Torch Geometric) comes with a steep learning curve. This NEExT package is a lot easier and supports NetworkX and iGraph, Wasserstein and more.
https://github.com/ashdehghan/NEExT
https://skolouri.github.io/wegl/
#GraphML #GraphAI

HackerNoon Sep 11, 2025

This article tests how degree, clustering, and topology–feature ties sway GNN and feature-only models using HypNF synthetic graphs. https://hackernoon.com/choose-the-right-graph-model-faster-with-hypnfs-parameter-knobs #graphml

Choose the Right Graph Model Faster with HypNF’s Parameter Knobs | HackerNoon

This article tests how degree, clustering, and topology–feature ties sway GNN and feature-only models using HypNF synthetic graphs.

HackerNoon Sep 10, 2025

Synthetic HypNF graphs reveal GNN fragilities: HGCN beats GCN on dense, homogeneous nets but falters on sparse power-law ones. https://hackernoon.com/a-hyperbolic-benchmark-for-stress-testing-gnns-across-degree-clustering-and-homophily #graphml

A Hyperbolic Benchmark for Stress-Testing GNNs Across Degree, Clustering, and Homophily | HackerNoon

Synthetic HypNF graphs reveal GNN fragilities: HGCN beats GCN on dense, homogeneous nets but falters on sparse power-law ones.

Sadamori May 28, 2024

🚨Preprint Alert🚨 Benchmarks guide #MachineLearning, but is the core benchmark for #GraphML, the link prediction task, guiding us correctly? Our latest preprint questions its validity, reveals a bias that substantially skews the evaluation, and proposes a degree-corrected link prediction benchmark. Let's dive in! https://arxiv.org/abs/2405.14985 More on 👉 https://twitter.com/skojaku/status/1795413358818013431

Implicit degree bias in the link prediction task

Link prediction -- a task of distinguishing actual hidden edges from random unconnected node pairs -- is one of the quintessential tasks in graph machine learning. Despite being widely accepted as a universal benchmark and a downstream task for representation learning, the validity of the link prediction benchmark itself has been rarely questioned. Here, we show that the common edge sampling procedure in the link prediction task has an implicit bias toward high-degree nodes and produces a highly skewed evaluation that favors methods overly dependent on node degree, to the extent that a ``null'' link prediction method based solely on node degree can yield nearly optimal performance. We propose a degree-corrected link prediction task that offers a more reasonable assessment that aligns better with the performance in the recommendation task. Finally, we demonstrate that the degree-corrected benchmark can more effectively train graph machine-learning models by reducing overfitting to node degrees and facilitating the learning of relevant structures in graphs.

arXiv.org

me Oct 30, 2023

@tml @Sweetshark @boris

used yEd couple times. Very flexible and export functions.
Liked their layout algorithms for objects en connector routing styles.

https://www.yworks.com/products/yed

#yed #graph #graphml

yEd Graph Editor

yEd is a free desktop application to quickly create, import, edit, and automatically arrange diagrams. It runs on Windows, macOS, and Unix/Linux.

yWorks, the diagramming experts

José Oramas Jun 26, 2023

Following some requests, the deadline for #AIMLAI at ECML-PKDD'23 has been extended until 30/06.
Looking forward to receive your submissions.

Call for papers: https://lnkd.in/eWvp74t8
Website: https://lnkd.in/eeZNFSZG

#xai #ai #ml #graphml #machinelearning #artificialintelligence #intepretability

AIMLAI@ECML/PKDD 2023 : Joint Tutorial on Explainable GraphML and International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence

José Oramas Jun 22, 2023

Less than one week for the submission deadline of #AIMLAI at ECML-PKDD'23. Looking forward to receive your short and long papers.

The workshop will be complemented by a great keynote speaker and a tutorial on Explainable GraphML.

Call for papers: https://lnkd.in/eWvp74t8
Website: https://lnkd.in/eeZNFSZG
#xai #ai #ml #graphml #machinelearning #artificialintelligence #intepretability

AIMLAI@ECML/PKDD 2023 : Joint Tutorial on Explainable GraphML and International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence

José Oramas May 23, 2023

Happy to announce that the 6th edition of the workshop on Advances in Interpretable Machine Learning and Artificial Intelligence Advances will be held this year jointly with ECML/PKDD 2023.

This year the workshop will be complemented with a tutorial on Explainable Graph-ML.

Deadline: June 27, 2023.

Call for papers: https://lnkd.in/eWvp74t8

Website: https://lnkd.in/eeZNFSZG

#machinelearning #artificialintelligence #ml #xai #graphml

AIMLAI@ECML/PKDD 2023 : Joint Tutorial on Explainable GraphML and International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence