# Exploring Graph Based Approaches for Author Name Disambiguation

arXiv: https://arxiv.org/abs/2312.08388 · HTML: https://mlsanity.com/paper/2312.08388/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 12 Dec 2023.

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

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Footwear Size Recommendation System](https://mlsanity.com/paper/1806.11423/) (2018)
- [One Embedding To Do Them All](https://mlsanity.com/paper/1906.12120/) (2019)
- [Name Disambiguation in Anonymized Graphs using Network Embedding](https://mlsanity.com/paper/1702.02287/) (2017)
- [Classification for everyone : Building geography agnostic models for fairer recognition](https://mlsanity.com/paper/2312.02957/) (2023)
- [Exploiting citation networks for large-scale author name disambiguation](https://mlsanity.com/paper/1401.6157/) (2014)
- [The many faces of mobility: Using bibliometric data to measure the movement of scientists](https://mlsanity.com/paper/1803.03449/) (2018)
- [Name Disambiguation from link data in a collaboration graph using temporal and topological features](https://mlsanity.com/paper/1406.5162/) (2014)
- [Quantifying the Diaspora of Knowledge in the Last Century](https://mlsanity.com/paper/1604.00696/) (2016)
- [Exploring the Evolution of Node Neighborhoods in Dynamic Networks](https://mlsanity.com/paper/1704.07171/) (2017)

## Built on by (most cited first, 4 of 4)

- [Shaping Political Discourse using multi-source News Summarization](https://mlsanity.com/paper/2312.11703/) (2023)
- [Zero-shot Active Learning Using Self Supervised Learning](https://mlsanity.com/paper/2401.01690/) (2024)
- [Detecting anxiety from short clips of free-form speech](https://mlsanity.com/paper/2312.15272/) (2023)
- [Exploring intra-task relations to improve meta-learning algorithms](https://mlsanity.com/paper/2312.16612/) (2023)

## Abstract (arXiv)

In many applications, such as scientific literature management, researcher search, social network analysis and etc, Name Disambiguation (aiming at disambiguating WhoIsWho) has been a challenging problem. In addition, the growth of scientific literature makes the problem more difficult and urgent. Although name disambiguation has been extensively studied in academia and industry, the problem has not been solved well due to the clutter of data and the complexity of the same name scenario. In this work, we aim to explore models that can perform the task of name disambiguation using the network structure that is intrinsic to the problem and present an analysis of the models.