Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGraphEmb, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGraphEmb achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.

Authors

• Yunsheng Bai
• Hao Ding
• Yang Qiao Shandong University
• Agustin Marinovic
• Ken Gu
• Ting Chen BNRist, Department of Computer Science and Technology, Tsinghua University
• Yizhou Sun University of California, Los Angeles
• Wei Wang

Keywords

• Machine Learning Applications: Networks
• Machine Learning: Unsupervised Learning