Multi-Task Graph Autoencoders

7 Nov 2018Phi Vu Tran

We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link prediction and semi-supervised node classification. Our simple, yet effective and versatile model is efficiently trained end-to-end in a single stage, whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize.

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Task Dataset Model Metric name Metric value Global rank Compare
Node Classification Citeseer MTGAE Accuracy 71.80% # 4
Link Prediction Citeseer MTGAE Accuracy 94.90% # 1
Node Classification Cora MTGAE Accuracy 79.00% # 6
Link Prediction Cora MTGAE Accuracy 94.60% # 1
Node Classification Pubmed MTGAE Accuracy 80.40% # 1
Link Prediction Pubmed MTGAE Accuracy 94.40% # 2