SGB-Net: Scalable Graph Broad Network.

Abstract

Due to the complexity and self-evolutionary property of graph data in reality, graph learning methods require both validity to represent unstructured data and scalability to adapt to evolving graphs. However, current works have representation learning limitations on optimizable graph feature space due to the bottleneck of the structure depth. Moreover, they encounter a complete retraining process when graphs evolve, especially in the case without the assistance of new labels. To address the above issues, we propose a scalable graph broad network (SGB-Net), which contains three proposed modules: the graph feature broad transformation layer (GFBT layer) for enhancing graph embedding and two update algorithms (SGB-Net-U, SGB-Net-S) for endowing scalability. The GFBT layer aims to explicitly expand the graph feature space and broadly build the model. It constructs two expandable feature spaces in various graph scales to embed graphs discriminatively. SGB-Net-U is an exploratory method designed to tackle the label-free graph incremental learning (GIL) problem by leveraging unsupervised incremental knowledge to expand graph representation. SGB-Net-S endows scalability in classical incremental learning scenarios involving labels. Benefiting from its broad construction framework, SGB-Net not only enhances graph embeddings but also seamlessly adapts and improves performance in response to graph expansion without requiring retraining. In the experiments conducted on 15 benchmark datasets, SGB-Net outperforms state-of-the-art GNNs in terms of both effectiveness and scalability.