Inferential SIR-GN: Scalable Graph Representation Learning

TL;DR

Inferential SIR-GN is a scalable graph representation learning model that captures structural roles of nodes, performs well on classification tasks, and is efficient for very large networks.

Contribution

It introduces a pre-trained model on random graphs that rapidly computes node representations, including for unseen nodes, focusing on structural role preservation.

Findings

Effective at node and graph classification tasks

Capable of handling very large graphs efficiently

Captures structural role information accurately

Abstract

Graph representation learning methods generate numerical vector representations for the nodes in a network, thereby enabling their use in standard machine learning models. These methods aim to preserve relational information, such that nodes that are similar in the graph are found close to one another in the representation space. Similarity can be based largely on one of two notions: connectivity or structural role. In tasks where node structural role is important, connectivity based methods show poor performance. Recent work has begun to focus on scalability of learning methods to massive graphs of millions to billions of nodes and edges. Many unsupervised node representation learning algorithms are incapable of scaling to large graphs, and are unable to generate node representations for unseen nodes. In this work, we propose Inferential SIR-GN, a model which is pre-trained on random graphs, then computes node representations rapidly, including for very large networks. We demonstrate that the model is able to capture node's structural role information, and show excellent performance at node and graph classification tasks, on unseen networks. Additionally, we observe the scalability of Inferential SIR-GN is comparable to the fastest current approaches for massive graphs.