About Graph Degeneracy, Representation Learning and Scalability

TL;DR

This paper introduces methods leveraging K-Core Decomposition to improve the scalability of graph representation learning algorithms, reducing their computational costs while maintaining embedding quality.

Contribution

It proposes two novel techniques that utilize graph degeneracy to enhance the efficiency of walk-based graph embedding methods.

Findings

Significant reduction in time and memory usage.

Maintained embedding quality comparable to traditional methods.

Effective on multiple academic datasets.

Abstract

Graphs or networks are a very convenient way to represent data with lots of interaction. Recently, Machine Learning on Graph data has gained a lot of traction. In particular, vertex classification and missing edge detection have very interesting applications, ranging from drug discovery to recommender systems. To achieve such tasks, tremendous work has been accomplished to learn embedding of nodes and edges into finite-dimension vector spaces. This task is called Graph Representation Learning. However, Graph Representation Learning techniques often display prohibitive time and memory complexities, preventing their use in real-time with business size graphs. In this paper, we address this issue by leveraging a degeneracy property of Graphs - the K-Core Decomposition. We present two techniques taking advantage of this decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms. We evaluate the performances, expressed in terms of quality of embedding and computational resources, of the proposed techniques on several academic datasets. Our code is available at https://github.com/SBrandeis/kcore-embedding