  Graph Representation Learning (GRL) methods have impacted fields from
chemistry to social science. However, their algorithmic implementations are
specialized to specific use-cases e.g.message passing methods are run
differently from node embedding ones. Despite their apparent differences, all
these methods utilize the graph structure, and therefore, their learning can be
approximated with stochastic graph traversals. We propose Graph Traversal via
Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the
implementation of diverse graph algorithms and enabling transparent and
efficient scaling to large graphs. GTTF is founded upon a data structure
(stored as a sparse tensor) and a stochastic graph traversal algorithm
(described using tensor operations). The algorithm is a functional that accept
two functions, and can be specialized to obtain a variety of GRL models and
objectives, simply by changing those two functions. We show for a wide class of
methods, our algorithm learns in an unbiased fashion and, in expectation,
approximates the learning as if the specialized implementations were run
directly. With these capabilities, we scale otherwise non-scalable methods to
set state-of-the-art on large graph datasets while being more efficient than
existing GRL libraries - with only a handful of lines of code for each method
specialization. GTTF and its various GRL implementations are on:
https://github.com/isi-usc-edu/gttf.
