Siamese Network Based Multiscale Self-Supervised Heterogeneous Graph Representation Learning

doi:10.1109/ACCESS.2022.3187088

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Siamese Network Based Multiscale Self-Supervised Heterogeneous Graph Representation Learning

2022-01-01

DOI：10.1109/ACCESS.2022.3187088

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Abstract

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Owing to label-free modeling of complex heterogeneity, self-supervised heterogeneous graph representation learning (SS-HGRL) has been widely studied in recent years. The goal of SS-HGRL is to design an unsupervised learning framework to represent complicated heterogeneous graph structures. However, based on contrastive learning, most existing methods of SS-HGRL require a large number of negative samples, which significantly increases the computation and memory costs. Furthermore, many methods cannot fully extract knowledge from a heterogeneous graph. To learn global and local information simultaneously at low time and space costs, we propose a novel Siamese Network based Multi-scale bootstrapping contrastive learning approach for Heterogeneous graphs (SNMH). Specifically, we first obtain views under the meta-path schema and the 1-hop relation type schema through dual-schema view generation. Then, we propose cross-schema and cross-view bootstrapping contrastive objectives to maximize the similarity of node representations between different schemas and views. By integrating and optimizing the above objectives, we can extract local and global information and eventually obtain the node representations for downstream tasks. To demonstrate the effectiveness of our model, we conduct experiments on several public datasets. Experimental results show that our model is superior to the state-of-the-art methods on the premise of lower time and space complexity. The source code and datasets are publicly available at https://github.com/lorisky1214/SNMH.

Keywords:

Representation learning

Costs

Learning systems

Data mining

Semantics

Self-supervised learning

Complexity theory

Graph theory

Heterogeneous graph

graph representation learning

self-supervised learning