Graph Neural Network

In this account we try to post the latest news and general information about graph neural networks and representation learning.

[Graph Neural Network @Graph_NN]

@rheanamariee @Hummmmmbaby @itsjulieromero Hi @rheanamariee! Thanks for organizing the event, we got great feedback from our PhD friends who attended!

[Graph Neural Network @Graph_NN]

@TheYotg Cool image! What is this graph?

[Dr. Emily Kubicek 💻🧠 @steministemily]

If you see me here posting about graphs and you’re new to data science, do not fret! I had ZERO idea what graphs were until a few months ago. But now I can’t imagine my ds work without them. Here is a simple (3 sentence) explanation to get you started: https://t.co/BZbY8F7bCW

[Graph Neural Network @Graph_NN]

@steministemily I am graph neural network and I approve this message! Go GNN!

[Connected Data @Connected_Data]

.@chamii22 's #KnowCon2020 #presentation "GraphEDM: A Unified Framework for #MachineLearning on #Graphs"; a compelling case about it's power to bridge #networkembedding & graph regularized #neuralnetworks

Talk 👉 https://t.co/3eDBvnW76T

Special Offers 👉 https://t.co/b4B1XVzGkS

[Graph Neural Network @Graph_NN]

A bit old, but great survey! And it is now updated! This is one of those posts that worth going through the comments to find good reviews and comments. Thanks @chamii22

[Ines Chami @chamii22]

Working with graph-structured data? Check out our recent survey for Machine Learning on Graphs:
https://t.co/UMXerbgvjp

We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.

[Ines Chami @chamii22]

GraphEDM encapsulates over thirty graph embedding methods: from graph regularization algorithms (Label Propagation, ...) to more recent advances such as random walks (DeepWalk, node2vec, ...) or GNNs (GCN, GAT, ...).

[Ines Chami @chamii22]

Working with graph-structured data? Check out our recent survey for Machine Learning on Graphs:
https://t.co/UMXerbgvjp

We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.

[Graph Neural Network @Graph_NN]

@PetarV_93 @pl219_Cambridge @Cambridge_Uni @williamleif @joanbruna @mmbronstein @TacoCohen Thanks for making your material available online #education4everyone

[Petar Veličković @PetarV_93]

Alongside @pl219_Cambridge, I'll be teaching a Master's module on GNNs @Cambridge_Uni. Feels surreal to be on the (virtual) other side 😊

Based on @williamleif's book + my ongoing work w/ @joanbruna @mmbronstein @TacoCohen. Hope to release materials soon! https://t.co/bupyj89ACL

[Graph Neural Network @Graph_NN]

@balcilar_m Happy to see graphs are making such a noticeable impact :) Congrats!

[Mark Heimann @lastgoodcaesar]

Structural role-based node embedding: extensive benchmarks, insights, and easy-to-use codebase. Led by undergrad alum Junchen Jin whose organization awes me 🙂 with Di Jin and @danaikoutra.
https://t.co/KsNMOC5WHX
https://t.co/FljLFGOitZ

[Jiaqi Ma @Jiaqi_Ma_]

Excited to share that we are organizing a workshop on Graph Learning Benchmarks (GLB) at the WebConf 2021 (#WWW2021)! Submission due on Feb. 15, 2021.
Website: https://t.co/2EQ6EW4qBS
Joint w/ @jiong971, Yuxiao Dong, @danaikoutra, @meiqzh.
1/4

[icpr2020milan @icpr2020milan]

Thanks to #ICPR2020 Keynote Speaker Prof Max Welling @wellingmax ( @UvA_Amsterdam ) for the talk “Equivariant Graph #NeuralNetworks and their Applications” 1/14 3PM @underlineio #ICPR2020Milan #IAPR #PatternRecognition #MachineLearning #DeepLearning #AI
https://t.co/3XuPxEGMaU

[Graph Neural Network @Graph_NN]

ICLR-2021 #workshop?

"Geometrical and Topological Representation Learning" workshop submission is not open yet, but the suggested deadline by ICLR are:

Submission for contribution: 26 February 2021
Mandatory Accept/Reject Notification: Mar 26, 2021

https://t.co/BNqPj4Dnkh

[Graph Neural Network @Graph_NN]

ICLR 2021 results are out!

Looking forward to the list of accepted papers to start generating those insightful figures.

Hopefully we will soon start to briefly review the accepted papers in this account.

#ICLR #ICLR2021 #GraphNN #GNN #GCN #RepresentationLearning #Graph

[ICLR @iclr_conf]

Congratulations to all authors of accepted #ICLR2021 papers! 🎉

Notifications are being sent out today. We had 2997 submissions, of which 860 papers have been accepted. Of these, there will be 53 Oral, 114 Spotlight and 693 Poster presentations.

[Michal Valko @misovalko]

Seven years ago, with Daniele, Pierre, and @omardrwch
we created "Graphs in Machine Learning" for https://t.co/8ZsGCgMwFN, first of its kind. From now, the future is Daniele at https://t.co/Eo7qZPVKYR! All the past material: https://t.co/UyR7h5etpn @UnivParisSaclay @DeepMind

[Eurographics Conference @EurographicsC]

#Eurographics2021 will feature a tutorial on Inverse Computational Spectral Geometry!
The presenters are @EmanueleRodola, Simone Melzi, Luca Cosmo (Sapienza University of Rome), @mmbronstein (Imperial College London), and Maks Ovsjanikov (LIX, Ecole Polytechnique, IP Paris)

[Graph Neural Network @Graph_NN]

-Slides https://t.co/jiF2KWvPco
- Presentation https://t.co/0mULg6Lioz

[Graph Neural Network @Graph_NN]

Temporal graphs!
Interested? here are a few notes:

- Read: "Temporal Graph Networks ..." (aka TGN) ICML 2020 workshop by @emaros96
- Code: available on GitHub, good practice if u want to start
not using DGL or PyG
- The code is now available on PyG thanks to @rusty1s