24 citations · 41 across the 3 of their papers we have counts for
4 papers
HUGE: Huge Unsupervised Graph Embeddings with TPUs
Brandon Mayer, Anton Tsitsulin, Hendrik Fichtenberger +2
Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
GraphWorld: Fake Graphs Bring Real Insights for GNNs
John Palowitch, Anton Tsitsulin, Brandon Mayer +1
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful…
Kartta Labs: Collaborative Time Travel
Sasan Tavakkol, Feng Han, Brandon Mayer +4
We introduce the modular and scalable design of Kartta Labs, an open source, open data, and scalable system for virtually reconstructing cities from historical maps and photos. Kar…