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: geeksforgeeks.org/graph-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 👉 knowledgeconnexions.world/ta… Special Offers 👉 knowledgeconnexions.world/ch…

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

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, ...).

Working with graph-structured data? Check out our recent survey for Machine Learning on Graphs: arxiv.org/pdf/2005.03675.pdf We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.

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! cl.cam.ac.uk/teaching/2021/R…

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. arxiv.org/abs/2101.05730 github.com/GemsLab/StrucEmbe…

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: graph-learning-benchmarks.gi… Joint w/ @jiong971, Yuxiao Dong, @danaikoutra, @meiqzh. 1/4

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 micc.unifi.it/icpr2020/index…

Seven years ago, with Daniele, Pierre, and @omardrwch we created "Graphs in Machine Learning" for master-mva.com, first of its kind. From now, the future is Daniele at bit.ly/38zjExn! All the past material: bit.ly/35wumTn @UnivParisSaclay @DeepMind

#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)

TGN (Temporal Graph Network) is now on Pytorch Geometric! Huge thanks @rusty1s for making this happen! It is the first model for dynamic graphs in PyG. Let's hope to have many more in the future! Example code: github.com/rusty1s/pytorch_g… Original Paper: arxiv.org/abs/2006.10637