On Generalizing Neural Node Embedding Methods to Multi-Network Problems

By Mark Heimann. 2017

How researchers see this paper

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

1. Yunsheng Bai
2. Haoyang Ding
3. Yang Qiao
4. Agustin Marinovic
5. Ken Gu
6. Tingting Chen
7. Yizhou Sun
8. Wei Wang

Citations in the corpus

• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019, background): “Theoretical analysis shows that many of these works cannot handle embeddings for multiple graphs in the sense that the node embeddings in one graph are not comparable to those in another graph in any straightforward way [Heimann and Koutra, 2017].”