Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

02/08/2021
by   Elan Markowitz, et al.
11

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/07/2022

pyGSL: A Graph Structure Learning Toolkit

We introduce pyGSL, a Python library that provides efficient implementat...
research
03/10/2023

Exphormer: Sparse Transformers for Graphs

Graph transformers have emerged as a promising architecture for a variet...
research
05/18/2023

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

Recent years have witnessed the rapid development of heterogeneous graph...
research
10/12/2021

GraPE: fast and scalable Graph Processing and Embedding

Graph Representation Learning methods have enabled a wide range of learn...
research
02/20/2018

Scalable Label Propagation for Multi-relational Learning on Tensor Product Graph

Label propagation on the tensor product of multiple graphs can infer mul...
research
08/23/2018

Learning Human-Object Interactions by Graph Parsing Neural Networks

This paper addresses the task of detecting and recognizing human-object ...

Please sign up or login with your details

Forgot password? Click here to reset