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PyTorch Geometric Temporal received a Best Paper Nomination at CIKM 2021. Special thanks to Paul Scherer, Yixuan He, George Panagopoulos, Maria Astefanoaei, Olivér Kiss, Ferenc Beres, Nico Collignon, Rik Sarkar. Paper: bit.ly/3jDgyOa Git: bit.ly/3EknBTw

We won Best Resource paper at CIKM 2021! Congratulations to Paul Scherer, Yixuan He, George Panagopoulos, Maria Astefanoaei, Olivér Kiss, Ferenc Beres, Nico Collignon and most of all, to Benedek Rozemberczki.

PyTorch Geometric Temporal received the Resource Track Best Paper Award CIKM 2021!! Special thanks to Paul Scherer, Yixuan He, George Panagopoulos, Maria Astefanoaei, Olivér Kiss, Ferenc Beres, Nico Collignon, Rik Sarkar. Paper: bit.ly/3jDgyOa Git: bit.ly/31vNW2V

Happy to share that our #SDM2022 paper SSSNET: Semi-Supervised Signed Network Clustering has been accepted! Special thanks to Gesine Reinert , Songchao Wang and Mihai Cucuringu.😊 Paper: arxiv.org/pdf/2110.06623… Code: github.com/SherylHYX/SSSN… #GNN #clustering #DeepLearning

PyTorch Geometric Signed Directed is out! Head over to our paper : arxiv.org/abs/2202.10793, GitHub: github.com/SherylHYX/pyto… and documentation: …metric-signed-directed.readthedocs.io/en/latest/ to find out more! Thanks to Xitong Zhang , Junjie Huang, Mihai Cucuringu and Gesine Reinert . #gnn #datascience

Excited to share that our paper "GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks" is accepted by #ICML2022, thanks to Gesine Reinert and other coauthors! Paper: arxiv.org/abs/2202.00211 Code: github.com/SherylHYX/GNNR… #GNN #deeplearning

Excited to share that our paper osmanastats "MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian" is accepted by #LoG2022, thanks to Gesine Reinert and other coauthors! Paper: arxiv.org/abs/2209.00546 Code: github.com/SherylHYX/MSGNN #GNN #PyTorch

Excited to share that our paper osmanastats "DIGRAC: Digraph Clustering Based on Flow Imbalance" is accepted by #LoG2022, thanks to Gesine Reinert and Mihai Cucuringu! Paper: arxiv.org/abs/2106.05194 Code: github.com/SherylHYX/DIGR… #GNN #PyTorch

We have a wonderful lineup of EIGHT tutorials this year! 🎉 Be sure to check them out on Dec 10 and 11! You must be registered in the main LoG conference to participate. For more information: logconference.org/schedule-tutor…

And the final guest lecture concluding the Oxford GDL course is by ⁦Petar Veličković⁩

Oxford showed a truly magical side! Visiting the Oxford Mathematics and Oxford Comp Sci departments was awe-inspiring & I was honoured to share our* recent research * Josh Southern Jeremy Wayland Corinna Coupette Michael Bronstein Thanks Yixuan He & Michael Bronstein for having me! 🙏🙏🙏

Excited to share that PyTorch Geometric Signed Directed has been accepted by #LoG2023, thanks to Xitong Zhang, Junjie Huang, Benedek Rozemberczki, Mihai Cucuringu, and Gesine Reinert! Paper: arxiv.org/abs/2202.10793 Code:github.com/SherylHYX/pyto… #opensource #signed #directed #gnn #pyg

Excited to share that our paper "Robust Angular Synchronization via Directed Graph Neural Networks" has been accepted by #ICLR2024, thanks to Gesine Reinert, David Wipf, and Mihai Cucuringu! Paper: arxiv.org/pdf/2310.05842… Code: github.com/SherylHYX/GNN_… #GNN #digraphs #network

Tomorrow, Thurs 25, 11:30 a.m. CEST — 1 p.m. CEST, presenting our work at #ICML2024, "Generalization Error of Graph Neural Networks in the Mean-field Regime" Location: Hall C 4-9, Poster 508,

Thanks everyone for coming to the Learning on Graphs Conference 2024 local meetup in Arizona today!

You are invited to ASU's 2025 Machine Learning Day on April 11th (TOMORROW), with 20 speakers, including keynotes Jyh-Jing Hwang and Dr. Jon Kleinberg. Register TODAY at asuevents.asu.edu/event/machine-…! Andrew Maynard Hua Wei Spring Berman Marios Papachristou Yingheng Wang Pooja Chitre

It is great to see so many of you at the 6th Machine Learning Day Arizona State University, either in person or virtually! Thanks to all the presenters for their excellent talks and to the audience for interesting questions & discussions! Hope to see you again soon! #MachineLearning