# Improving Textual Network Embedding with Global Attention via Optimal Transport

arXiv: https://arxiv.org/abs/1906.01840 · HTML: https://mlsanity.com/paper/1906.01840/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [TOT: Topology-Aware Optimal Transport For Multimodal Hate Detection](https://mlsanity.com/paper/2303.09314/) (2023)
- [Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering](https://mlsanity.com/paper/2205.06530/) (2022)
- [COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport](https://mlsanity.com/paper/2406.12304/) (2024)

## Abstract (arXiv)

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.