Hebbian Graph Embeddings

TL;DR

This paper introduces a scalable, parallelizable graph embedding method based on Hebbian learning rules, modeling node embeddings as Gaussian mixtures, and demonstrates its effectiveness on benchmarks and real-world recommendation tasks.

Contribution

It presents a novel Hebbian learning-based algorithm for graph embeddings that is highly scalable and suitable for large, high-dimensional graphs.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Easily parallelizable without shared memory.

Effective in generating recommendations for large-scale retail data.

Abstract

Representation learning has recently been successfully used to create vector representations of entities in language learning, recommender systems and in similarity learning. Graph embeddings exploit the locality structure of a graph and generate embeddings for nodes which could be words in a language, products of a retail website; and the nodes are connected based on a context window. In this paper, we consider graph embeddings with an error-free associative learning update rule, which models the embedding vector of node as a non-convex Gaussian mixture of the embeddings of the nodes in its immediate vicinity with some constant variance that is reduced as iterations progress. It is very easy to parallelize our algorithm without any form of shared memory, which makes it possible to use it on very large graphs with a much higher dimensionality of the embeddings. We study the efficacy of proposed method on several benchmark data sets and favorably compare with state of the art methods. Further, proposed method is applied to generate relevant recommendations for a large retailer.