Workshop on Graph Learning Benchmarks
@glb_workshop
The official Twitter account for the workshop on Graph Learning Benchmarks.
ID: 1475954702261141507
https://graph-learning-benchmarks.github.io/ 28-12-2021 22:20:20
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🚨 We're happy to share one of our submissions to Workshop on Graph Learning Benchmarks outlining new, more comparable link prediction metrics than MR, MRR, and Hits@K including the zMR, zMRR, and zHits@K Thanks to authors @cthoyt Max Berrendorf Michael Galkin Volker Tresp Benjamin Gyori
Excited to announce our keynote speakers on April 26th! Prof. Michael Bronstein (Michael Bronstein), Prof. Tina Eliassi-Rad (@tinaeliassi), and Prof. Stephan Günnemann (Stephan Günnemann). The workshop program is coming soon. Please stay tuned on our website: graph-learning-benchmarks.github.io/glb2022
Our @cthoyt and will be presenting our recent manuscript on a unified framework for rank-based evaluation metrics in PyKEEN at Workshop on Graph Learning Benchmarks. Watch it here: 📺 Recording: youtube.com/watch?v=IlRKU3… 📜 Paper: arxiv.org/abs/2203.07544 📊Code and Data: github.com/pykeen/ranking…
We are delighted to announce our amazing invited panelists: Xin Luna Dong, Petar Veličković (Petar Veličković), Minjie Wang (Minjie Wang), and Rose Yu (Rose Yu)! Our workshop program is also online on our website: graph-learning-benchmarks.github.io Look forward to seeing you on Apr 26th!
We are thrilled to announce that, Prof. Jimeng Sun from UIUC, Prof. Yizhou Sun from UCLA, Prof. Xavier Bresson from NUS, and Dr. Minjie Wang from the AWS DGL team will be our invited speakers! Looking forward to their talks!
Also, we are lucky to have Dr. Neil Shah from Snap, Prof. @YujunYan4 from Dartmouth, and Dr. Michael Galkin from Intel as our invited panelists. Look forward to an insightful panel discussion from them!
Michael Galkin Workshop on Graph Learning Benchmarks Glad to finally meet you this August!
Tomorrow (08/06, 11.20 PT) Claudio Pomo will give a presentation at Workshop on Graph Learning Benchmarks at #KDD2023 on how we integrated six SOTA #graph-based #recsys into #ELLIOT and dockerized them 🐬💻 **DISCLAIMER** This may contain spoilers about our upcoming #reproducibility paper at #RecSys2023 😎 👇
A work w/ Claudio Pomo, Walter Anelli, Tommaso Di Noia, Antonio Ferrara GitHub Demo: tinyurl.com/yndv73yt Paper at Workshop on Graph Learning Benchmarks (with spoilers of the #RecSys2023 paper): tinyurl.com/4ajh36ms