# Predicting Links on Wikipedia with Anchor Text Information

arXiv: https://arxiv.org/abs/2105.11734 · HTML: https://mlsanity.com/paper/2105.11734/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 12 of 12)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [Evaluating Link Prediction Methods](https://mlsanity.com/paper/1505.04094/) (2015)
- [Global Vectors for Node Representations](https://mlsanity.com/paper/1902.11004/) (2019)
- [Improving Website Hyperlink Structure Using Server Logs](https://mlsanity.com/paper/1512.07258/) (2015)
- [Inductive Document Network Embedding with Topic-Word Attention](https://mlsanity.com/paper/2001.03369/) (2020)
- [Link Prediction with Mutual Attention for Text-Attributed Networks](https://mlsanity.com/paper/1902.11054/) (2019)
- [Document Network Projection in Pretrained Word Embedding Space](https://mlsanity.com/paper/2001.05727/) (2020)
- [Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia](https://mlsanity.com/paper/1503.04208/) (2015)

## Built on by (most cited first, 1 of 1)

- [Anchor Prediction: A Topic Modeling Approach](https://mlsanity.com/paper/2205.14631/) (2022)

## Abstract (arXiv)

Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of documents bound together by internal hyperlinks. These links form the building blocks of a large network whose structure contains important information on the concepts covered in this encyclopedia. The presence of a link between two articles, materialised by an anchor text in the source page pointing to the target page, can increase readers' understanding of a topic. However, the process of linking follows specific editorial rules to avoid both under-linking and over-linking. In this paper, we study the transductive and the inductive tasks of link prediction on several subsets of the English Wikipedia and identify some key challenges behind automatic linking based on anchor text information. We propose an appropriate evaluation sampling methodology and compare several algorithms. Moreover, we propose baseline models that provide a good estimation of the overall difficulty of the tasks.