# Name Disambiguation in Anonymized Graphs using Network Embedding

arXiv: https://arxiv.org/abs/1702.02287 · HTML: https://mlsanity.com/paper/1702.02287/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2017.

## Built on by (most cited first, 9 of 9)

- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals](https://mlsanity.com/paper/2201.09555/) (2022)
- [Exploring Graph Based Approaches for Author Name Disambiguation](https://mlsanity.com/paper/2312.08388/) (2023)
- [Whois? Deep Author Name Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2207.04772/) (2022)
- [Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives](https://mlsanity.com/paper/2212.12715/) (2022)
- [BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting](https://mlsanity.com/paper/2404.08322/) (2024)
- [Network Embedding via Deep Prediction Model](https://mlsanity.com/paper/2104.13323/) (2021)
- [Deep Author Name Disambiguation using DBLP Data](https://mlsanity.com/paper/2303.10067/) (2023)
- [Cross-Source Reasoning-based Correction for Author Name Disambiguation](https://mlsanity.com/paper/2606.08617/) (2026)

## Abstract (arXiv)

In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting.