mlsanityData to 14 Oct 2026

Exploring Graph Based Approaches for Author Name Disambiguation

arXiv 2312.08388 ↗First posted 12 Dec 2023cs.SI cs.CL cs.LGcited by 4

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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.

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

12 of 29 references.

References · 12

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  3. 2014Distributed Representations of Sentences and Documentscited by 678
  4. 2018Footwear Size Recommendation Systemcited by 10
  5. 2019One Embedding To Do Them Allcited by 10
  6. 2017Name Disambiguation in Anonymized Graphs using Network Embeddingcited by 9
  7. 2023Classification for everyone : Building geography agnostic models for fairer recognitioncited by 5
  8. 2014Exploiting citation networks for large-scale author name disambiguationcited by 4
  9. 2018The many faces of mobility: Using bibliometric data to measure the movement of scientistscited by 3
  10. 2014Name Disambiguation from link data in a collaboration graph using temporal and topological featurescited by 1
  11. 2016Quantifying the Diaspora of Knowledge in the Last Centurycited by 1
  12. 2017Exploring the Evolution of Node Neighborhoods in Dynamic Networkscited by 1

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Shaping Political Discourse using multi-source News Summarizationcited by 5
  2. 2024Zero-shot Active Learning Using Self Supervised Learningcited by 1
  3. 2023Detecting anxiety from short clips of free-form speechcited by 0
  4. 2023Exploring intra-task relations to improve meta-learning algorithmscited by 0