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

1 Apr 2019  ·  Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Graph Classification IMDb-M UGraphEmb Accuracy 50.06% # 18
Graph Classification IMDb-M UGraphEmb-F Accuracy 50.97% # 14
Graph Classification NCI109 UGraphEmb-F Accuracy 74.48 # 12
Graph Classification NCI109 UGraphEmb Accuracy 69.17 # 17
Graph Classification PTC UGraphEmb Accuracy 72.54% # 7
Graph Classification PTC UGraphEmb-F Accuracy 73.56% # 4
Graph Classification REDDIT-MULTI-12K UGraphEmb Accuracy 39.97 # 3
Graph Classification REDDIT-MULTI-12K UGraphEmb-F Accuracy 41.84 # 2
Graph Classification Web UGraphEmb-F Accuracy 45.03 # 1


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