# Inductive Document Network Embedding with Topic-Word Attention

arXiv: https://arxiv.org/abs/2001.03369 · HTML: https://mlsanity.com/paper/2001.03369/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

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

- [Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings](https://mlsanity.com/paper/2101.11059/) (2021)
- [Predicting Links on Wikipedia with Anchor Text Information](https://mlsanity.com/paper/2105.11734/) (2021)
- [Anchor Prediction: A Topic Modeling Approach](https://mlsanity.com/paper/2205.14631/) (2022)

## Abstract (arXiv)

Document network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding approaches by incorporating the text content associated with the nodes in their formulations. In most cases, it is hard to interpret the learned representations. Moreover, little importance is given to the generalization to new documents that are not observed within the network. In this paper, we propose an interpretable and inductive document network embedding method. We introduce a novel mechanism, the Topic-Word Attention (TWA), that generates document representations based on the interplay between word and topic representations. We train these word and topic vectors through our general model, Inductive Document Network Embedding (IDNE), by leveraging the connections in the document network. Quantitative evaluations show that our approach achieves state-of-the-art performance on various networks and we qualitatively show that our model produces meaningful and interpretable representations of the words, topics and documents.