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

By Yunsheng Bai, Haoyang Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Tingting Chen, Yizhou Sun, Wei Wang. 2019

86 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistical and Nonlinear Physics, Representation & Self-Supervised Learning, Graph Learning

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In Management Science and Operations Research

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In Graph Learning

Citations in the corpus

Strategies for Pre-training Graph Neural Networks (2019, background): “Examples of such tasks include modeling the graph edit distance (Bai et al., 2019) or predicting graph structure similarity (Navarin et al., 2018).”