Learning Graph Representations with Recurrent Neural Network Autoencoders

By Aynaz Taheri. 2018

49 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qingquan Song
2. Xia Hu
3. Xiao Huang
4. Kaixiong Zhou
5. Qiaoyu Tan
6. Ninghao Liu
7. Yi-Wei Chen
8. Mengnan Du

Citations in the corpus

• Auto-GNN: Neural architecture search of graph neural networks (2019, methodology): “Graph neural networks (GNN) [1, 2] has been demonstrated that it could achieve superior performance in modeling graph-structured data, within various domains such as social media [3–6] and bioinformatics [7, 8].”
• Deep Representation Learning for Social Network Analysis (2019, background): “, 2018) or concatenating (Taheri et al., 2018) these graph sequence embeddings.”