#graphml — Public Fediverse posts

#Higraph progress!

Still got lots to do, but hyperEdges can now be saved & loaded in modified #graphml files. The "model tree" on the left highlights items in the graph on the right.
I can see "minimum viable product"!

The #hyperedge structure is both graphically and algebraically accessible. I'm not aware of anything else that does this, pretty certainly not in #Python
#graphTheory #VisualFormalism

#Higraph progress!

Still got lots to do, but hyperEdges can now be saved & loaded in modified #graphml files. The "model tree" on the left highlights items in the graph on the right.
I can see "minimum viable product"!

The #hyperedge structure is both graphically and algebraically accessible. I'm not aware of anything else that does this, pretty certainly not in #Python
#graphTheory #VisualFormalism

#Higraph progress!

Still got lots to do, but hyperEdges can now be saved & loaded in modified #graphml files. The "model tree" on the left highlights items in the graph on the right.
I can see "minimum viable product"!

The #hyperedge structure is both graphically and algebraically accessible. I'm not aware of anything else that does this, pretty certainly not in #Python
#graphTheory #VisualFormalism

#Higraph progress!

Still got lots to do, but hyperEdges can now be saved & loaded in modified #graphml files. The "model tree" on the left highlights items in the graph on the right.
I can see "minimum viable product"!

The #hyperedge structure is both graphically and algebraically accessible. I'm not aware of anything else that does this, pretty certainly not in #Python
#graphTheory #VisualFormalism

#Higraph progress!

Still got lots to do, but hyperEdges can now be saved & loaded in modified #graphml files. The "model tree" on the left highlights items in the graph on the right.
I can see "minimum viable product"!

The #hyperedge structure is both graphically and algebraically accessible. I'm not aware of anything else that does this, pretty certainly not in #Python
#graphTheory #VisualFormalism

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Introduction to Graph Machine Learning

https://huggingface.co/blog/intro-graphml

In this blog post, we cover the basics of graph machine learning.
We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs.
#graphml

Introduction to Graph Machine Learning

https://huggingface.co/blog/intro-graphml

In this blog post, we cover the basics of graph machine learning.
We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs.
#graphml

Introduction to Graph Machine Learning

https://huggingface.co/blog/intro-graphml

In this blog post, we cover the basics of graph machine learning.
We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs.
#graphml

Introduction to Graph Machine Learning

https://huggingface.co/blog/intro-graphml

In this blog post, we cover the basics of graph machine learning.
We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs.
#graphml

Introduction to Graph Machine Learning

https://huggingface.co/blog/intro-graphml

In this blog post, we cover the basics of graph machine learning.
We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs.
#graphml

My first #introduction toot 😂 !
Hi, I'm a PhD candidate at the VU Amsterdam.
I'm interested in combining ML and specifically Graph ML approaches with background knowledge to predict novel, viable and interesting research hypotheses to investigate and propose research directions. Also into biomedical AI and apply my research on problems such as interaction prediction (DDI, DTI) and more.

#ai #machinelearning #deeplearning #graphml #bioinformatics #cheminformatics #scientificdiscovery

My first #introduction toot 😂 !
Hi, I'm a PhD candidate at the VU Amsterdam.
I'm interested in combining ML and specifically Graph ML approaches with background knowledge to predict novel, viable and interesting research hypotheses to investigate and propose research directions. Also into biomedical AI and apply my research on problems such as interaction prediction (DDI, DTI) and more.

#ai #machinelearning #deeplearning #graphml #bioinformatics #cheminformatics #scientificdiscovery

My first #introduction toot 😂 !
Hi, I'm a PhD candidate at the VU Amsterdam.
I'm interested in combining ML and specifically Graph ML approaches with background knowledge to predict novel, viable and interesting research hypotheses to investigate and propose research directions. Also into biomedical AI and apply my research on problems such as interaction prediction (DDI, DTI) and more.

#ai #machinelearning #deeplearning #graphml #bioinformatics #cheminformatics #scientificdiscovery

RT @[email protected]

Still wasting time drawing #UML diagrams to document your system?

Take the next step to #DocsAsCode and #DocsFromCode with our new tutorial on automatically & continuously generating reports as tables & diagrams!

#Asciidoc #PlantUML #GraphML #yEd #CSV

https://101.jqassistant.org/generate-reports-about-structures-and-metrics/

🐦🔗: https://twitter.com/jqassistant/status/1229337348896444418