Published in International Conference on Learning Representations, 2021
Meta-algorithm that can be used to re-implement a variety of machine learning algorithms on graphs. Once re-implemented in GTTF, algorithms automatically scale to large graphs. The meta-algorithm accepts two functions (BiasFn and AccumulateFn) and it repeatedly samples walk forests from graph, invoking BiasFn and AccumulateFn along the walks. Certain choices of these two functions will recover unbiased learning for a variety of machine learning algorithms on graphs, including many message passing (graph convolution) methods as well as node embedding methods.
Recommended citation: Markowitz, E. S., Balasubramanian, K., Mirtaheri, M., Abu-El-Haija, S., Perozzi, B., Ver Steeg, G., Galstyan, A. (2021). "Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning." International Conference on Learning Representations. 2021. https://openreview.net/forum?id=6DOZ8XNNfGN