Inferential SIR-GN: Scalable Graph Representation Learning
This work addresses the need for scalable unsupervised methods to handle massive graphs with millions to billions of nodes and edges, particularly for tasks where node structural role is important, representing an incremental improvement in efficiency and applicability.
The paper tackled the problem of scalable graph representation learning for capturing node structural roles, proposing Inferential SIR-GN, which is pre-trained on random graphs and rapidly computes node representations for large networks, showing excellent performance in node and graph classification tasks on unseen networks with scalability comparable to the fastest current approaches.
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.