short-paper

A Network-embedding Based Method for Author Disambiguation

Authors:

Yongquan FuAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 1735 - 1738

https://doi.org/10.1145/3269206.3269272

Published: 17 October 2018 Publication History

Abstract

Most existing author disambiguation work relies heavily on feature engineering or cannot use multiple paper relationships. In this work, we propose a network-embedding based method for author disambiguation. For each ambiguous name, we construct networks among papers sharing an ambiguous name, and connect papers with multiple relationships (e.g., co-authoring a paper). We focus on maximizing the gap between positive paper edges and negative edges, and propose a graph coarsening technique to learn global information. Further, we design a clustering algorithm which partitions paper representations into disjoint sets such that each set contains all papers of a unique author. Through extensive experiments, we show that our method is significantly better than the state-of-the-art author disambiguation and network-embedding methods.

References

[1]

Ricardo J. G. B. Campello, Davoud Moulavi, and Jörg Sander. 2013. Density-Based Clustering Based on Hierarchical Density Estimates. In PAKDD.

[2]

Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. GraRep: Learning Graph Representations with Global Structural Information. In CIKM.

Digital Library

[3]

Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena. 2018. HARP: Hierarchical Representation Learning for Networks. In AAAI.

[4]

Wei Sheng Chin, Yu Chin Juan, and Wei Cheng. 2013. Effective string processing and matching for author disambiguation. In KDD Cup.

[5]

Dorin Comaniciu and Peter Meer. 2002. Mean shift: a robust approach toward feature space analysis. PAMI 24, 5 (2002), 603--619.

Digital Library

[6]

Pelleg Dan and Andrew W. Moore. 2000. X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In ICML.

Digital Library

[7]

Brendan J. Frey and Delbert Dueck. 2007. Clustering by Passing Messages Between Data Points. Science 315, 5814 (2007), 972--976.

[8]

Tao-Yang Fu, Wang-Chien Lee, and Zhen Lei. 2017. HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. In CIKM.

Digital Library

[9]

A Grover and J Leskovec. 2016. node2vec: Scalable Feature Learning. In KDD.

Digital Library

[10]

Maria Halkidi, Michalis Vazirgiannis, and Yannis Batistakis. 2000. Quality Scheme Assessment in the Clustering Process. In PKDD.

Digital Library

[11]

Hui Han, Lee Giles, Hongyuan Zha, Cheng Li, and Kostas Tsioutsiouliklis. 2004. Two supervised learning approaches for name disambiguation in author citations. In Joint Conference on Digital Libraries (JCDL).

Digital Library

[12]

Madian Khabsa, Pucktada Treeratpituk, and C. Lee Giles. 2015. Online Person Name Disambiguation with Constraints. In JCDL.

Digital Library

[13]

Xueqin Lin, Fen Zhu, Bo Peng, and Weiling Li. 2017. A Novel Approach for Author Name Disambiguation Using Ranking Confidence. In DSFAA.

[14]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. Computer Science (2013).

[15]

Bryan Perozzi, Rami Alrfou, and Steven Skiena. 2014. DeepWalk: online learning of social representations. KDD (2014).

Digital Library

[16]

Yanan Qian, Junting Zheng, and Jun Liu. 2015. Author name disambiguation for growing digital libraries. Information Retrieval Journal 18, 5 (2015).

Digital Library

[17]

Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, and Jiawei Han. 2017. An Attention-based Collaboration Framework for Multi-View Network Representation Learning. In CIKM. 1767--1776.

Digital Library

[18]

Jie Tang, Alvis C. M. Fong, Bo Wang, and Jing Zhang. 2012. A Unified Probabilistic Framework for Name Disambiguation in Digital Library. TKDE 24, 6 (2012).

Digital Library

[19]

Jian Tang, Meng Qu, and Qiaozhu Mei. 2015. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. In WWW.

Digital Library

[20]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In KDD.

Digital Library

[21]

Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. 2017. CANE: ContextAware Network Embedding for Relation Modeling. In ACL. 1722--1731.

[22]

Xuezhi Wang, Jie Tang, Hong Cheng, and Philip S. Yu. 2011. ADANA: Active Name Disambiguation. In ICDM.

Digital Library

[23]

Baichuan Zhang, Murat Dundar, and Mohammad Al Hasan. 2016. Bayesian Non-Exhaustive Classification A Case Study:Online Name Disambiguation using Temporal Record Streams. In CIKM.

Digital Library

[24]

Baichuan Zhang and Mohammad Al Hasan. 2017. Name Disambiguation in Anonymized Graphs using Network Embedding. In CIKM.

Digital Library

Cited By

Huang CChen K(2024)RefCit2vec: embedding models considering references and citations for measuring document similarityScientometrics10.1007/s11192-024-05067-3Online publication date: 10-Jul-2024
https://doi.org/10.1007/s11192-024-05067-3
Bai SBu CWu X(2024)High‐degree penalty based global statistical network embedding for name disambiguation in anonymized graphConcurrency and Computation: Practice and Experience10.1002/cpe.8195Online publication date: 2-Jun-2024
https://doi.org/10.1002/cpe.8195
Gong JFang XWang CJu JBao YZhang JXu J(2023)Author Name Disambiguation based on Capsule Network via Semantic and Structural FeaturesProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614053(293-300)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3614008.3614053
Show More Cited By

Index Terms

A Network-embedding Based Method for Author Disambiguation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
    2. Natural language processing

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Published In

cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 17 October 2018

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National Basic Research Program of China
National Science Foundation for Young Scholars of China

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CIKM '18

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Citations

Cited By

Huang CChen K(2024)RefCit2vec: embedding models considering references and citations for measuring document similarityScientometrics10.1007/s11192-024-05067-3Online publication date: 10-Jul-2024
https://doi.org/10.1007/s11192-024-05067-3
Bai SBu CWu X(2024)High‐degree penalty based global statistical network embedding for name disambiguation in anonymized graphConcurrency and Computation: Practice and Experience10.1002/cpe.8195Online publication date: 2-Jun-2024
https://doi.org/10.1002/cpe.8195
Gong JFang XWang CJu JBao YZhang JXu J(2023)Author Name Disambiguation based on Capsule Network via Semantic and Structural FeaturesProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614053(293-300)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3614008.3614053
Boukhers ZAsundi N(2023)Deep author name disambiguation using DBLP dataInternational Journal on Digital Libraries10.1007/s00799-023-00361-625:3(431-441)Online publication date: 4-May-2023
https://doi.org/10.1007/s00799-023-00361-6
Wang SLi QKoopman R(2023)Co-attention-Based Pairwise Learning for Author Name DisambiguationLeveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration10.1007/978-981-99-8088-8_21(240-249)Online publication date: 30-Nov-2023
https://doi.org/10.1007/978-981-99-8088-8_21
Pooja KMondal SChandra J(2022)Exploiting Higher Order Multi-dimensional Relationships with Self-attention for Author Name DisambiguationACM Transactions on Knowledge Discovery from Data10.1145/350273016:5(1-23)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3502730
Xie WLiu SWang XJia T(2022)Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI56018.2022.00043(245-250)Online publication date: Oct-2022
https://doi.org/10.1109/ICTAI56018.2022.00043
Boukhers ZAsundi N(2022)Whois? Deep Author Name Disambiguation Using Bibliographic DataLinking Theory and Practice of Digital Libraries10.1007/978-3-031-16802-4_16(201-215)Online publication date: 15-Sep-2022
https://doi.org/10.1007/978-3-031-16802-4_16
Mel AKang BLijffijt JDe Bie T(2021)FONDUE: A Framework for Node Disambiguation and Deduplication Using Network EmbeddingsApplied Sciences10.3390/app1121988411:21(9884)Online publication date: 22-Oct-2021
https://doi.org/10.3390/app11219884
Fan CLi Y(2021)Chinese Personal Name Disambiguation Based on ClusteringWireless Communications & Mobile Computing10.1155/2021/37901762021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/3790176
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