Improving Textual Network Embedding with Global Attention via Optimal Transport

Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin


Abstract
Constituting highly informative network embeddings is an essential 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.
Anthology ID:
P19-1512
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5193–5202
Language:
URL:
https://aclanthology.org/P19-1512
DOI:
10.18653/v1/P19-1512
Bibkey:
Cite (ACL):
Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, and Lawrence Carin. 2019. Improving Textual Network Embedding with Global Attention via Optimal Transport. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5193–5202, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Improving Textual Network Embedding with Global Attention via Optimal Transport (Chen et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1512.pdf
Supplementary:
 P19-1512.Supplementary.pdf
Data
Cora