Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network-embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport, and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.
@article{arxiv.1906.01840,
title = {Improving Textual Network Embedding with Global Attention via Optimal Transport},
author = {Liqun Chen and Guoyin Wang and Chenyang Tao and Dinghan Shen and Pengyu Cheng and Xinyuan Zhang and Wenlin Wang and Yizhe Zhang and Lawrence Carin},
journal= {arXiv preprint arXiv:1906.01840},
year = {2019}
}