Weihua Hu: Super excited to share Open Graph Benchmark (OGB)! OGB provides large-scale, diverse graph datasets to catalyze graph ML research. The datasets are easily accessible via OGB Python package with unified evaluation protocols and public leaderboards.
Paper: https://arxiv.org/abs/2005.00687 https://t.co/5aGXqQxDJQ
2 replies, 432 likes
Weihua Hu: Excited to announce that "Open Graph Benchmark: Datasets for ML on Graphs" has been accepted to #NeurIPS as a spotlight!
If you are working on Graph ML, please check it out for your next conference submission :)
Paper: https://arxiv.org/abs/2005.00687 https://t.co/khoC7MsidR
6 replies, 297 likes
Weihua Hu: Check out our 2nd major release of OGB!
We have provided 5 new datasets, including a web-scale gigantic graph (with 100M+ nodes) and heterogeneous graphs.
1 replies, 58 likes
Denny Britz: Stanford’s Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning: https://ogb.stanford.edu/
They even have a leaderboard :)
Paper: https://arxiv.org/abs/2005.00687 https://t.co/96vehWu7Ux
0 replies, 46 likes
Marinka Zitnik: Subgraph Neural Networks
GNNGuard: Defending GNNs against Adversarial Attacks
Graph Meta Learning via Local Subgraphs
Open Graph Benchmark: Datasets for ML on Graphs
1 replies, 45 likes
Marinka Zitnik: We are thrilled to release the Open Graph Benchmark!
OGB contains numerous biomedical datasets, including protein interaction nets, cross-species graphs, drug-drug interaction nets, and biomedical knowledge graphs
#ML #graphs #networks
0 replies, 40 likes
Kristian Kersting: 🙏 for gathering this data. Glad that our previous @sfb876 work has helped to push this. Maybe, at some point, we can actually merge all graph datasets at a single location. Anyhow, great initiative. Thanks!
0 replies, 22 likes
Michele Catasta: Among the new tasks in this release, we included code summarization (AKA method naming): https://ogb.stanford.edu/docs/graphprop/#ogbg-code
The dataset is based on the great work done by @github @miltos1 @mmjb86 for the CodeSearchNet challenge.
Looking forward to your submissions!
0 replies, 11 likes
Weihua Hu: Our arXiv paper has also been updated: https://arxiv.org/abs/2005.00687
Includes detailed descriptions of each dataset, the extensive benchmark experiments, and discussion on research challenges and opportunities.
0 replies, 9 likes
Chaitanya Joshi: My favorite feature of the benchmark: the performance measure is for generalization outside of training distribution, similar to real-world problems. For example, learn from a social network before 2017, validate on 2018, and compare models on 2019.
0 replies, 7 likes
cs.LG Papers: Open Graph Benchmark: Datasets for Machine Learning on Graphs. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec http://arxiv.org/abs/2005.00687
1 replies, 1 likes
Found on May 05 2020 at https://arxiv.org/pdf/2005.00687.pdf