2018/11/07 by Phi Vu Tran, Tran, Phi Vu · 21 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Computer science #Economics #Epigenetics and DNA Methylation #FOS: Computer and information sciences #Graph #Graph Theory and Algorithms #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Management #Natural language processing #Task (project management) #Theoretical computer science #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1811.02798
published in arXiv (Cornell University) (Cornell University) · NIPS 2018 Workshop on Relational Representation Learning. Short version of arXiv:1802.08352
arxiv created 2018/11/07 · openalex publication_date 2018/11/07 · arxiv updated 2018/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. We provide an empirical evaluation of our model on five benchmark relational, graph-structured datasets and demonstrate significant improvement over three strong baselines for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning