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Table 4.

Approaches to author name disambiguation in 2011–2021

AuthorsYearApproachSupervised
Pooja, Mondal, and Chandra (2020)  2020 Graph-based combination of author similarity and topic graph ✗ 
Wang, Wang et al. (2020)  2020 Adversarial representation learning ✓ 
Kim, Kim, and Owen-Smith (2019)  2019 Matching email address, self-citation and coauthorship with iterative clustering ✗ 
Zhang, Xinhua, and Pan (2019)  2019 Hierarchical clustering with edit distances ✗ 
Ma, Wang, and Zhang (2019)  2019 Graph-based approach ✗ 
Kim, Rohatgi, and Giles (2019)  2019 Deep neural network ✓ 
Zhang, Yan, and Zheng (2019)  2019 Graph-based approach and clustering ✗ 
Zhang et al. (2019)  2019 Molecular cross clustering ✗ 
Xu, Li et al. (2018)  2018 Combination of single features ✓ 
Pooja, Mondal, and Chandra (2018)  2018 Rule-based clustering ✗ 
Sun et al. (2017)  2017 Multi-level clustering ✗ 
Lin, Zhu et al. (2017)  2017 Hierarchical clustering with combination of similarity metrics ✗ 
Müller (2017)  2017 Neural network using embeddings ✓ 
Kim, Khabsa, and Giles (2016)  2016 DBSCAN with random forest ✗ 
Momeni and Mayr (2016)  2016 Clustering based on coauthorship ✗ 
Protasiewicz and Dadas (2016)  2016 Rule-based heuristic, linear regression, support vector machines and AdaBoost ✓ 
Qian, Zheng et al. (2015)  2015 Support vector machines ✓ 
Tran, Huynh, and Do (2014)  2014 Deep neural network ✓ 
Caron and van Eck (2014)  2014 Rule-based scoring ✗ 
Schulz, Mazloumian et al. (2014)  2014 Pairwise comparison and clustering ✗ 
Kastner, Choi, and Jung (2013)  2013 Random forest, support vector machines and clustering ✓ 
Wilson (2011)  2011 Single layer perceptron ✓ 
AuthorsYearApproachSupervised
Pooja, Mondal, and Chandra (2020)  2020 Graph-based combination of author similarity and topic graph ✗ 
Wang, Wang et al. (2020)  2020 Adversarial representation learning ✓ 
Kim, Kim, and Owen-Smith (2019)  2019 Matching email address, self-citation and coauthorship with iterative clustering ✗ 
Zhang, Xinhua, and Pan (2019)  2019 Hierarchical clustering with edit distances ✗ 
Ma, Wang, and Zhang (2019)  2019 Graph-based approach ✗ 
Kim, Rohatgi, and Giles (2019)  2019 Deep neural network ✓ 
Zhang, Yan, and Zheng (2019)  2019 Graph-based approach and clustering ✗ 
Zhang et al. (2019)  2019 Molecular cross clustering ✗ 
Xu, Li et al. (2018)  2018 Combination of single features ✓ 
Pooja, Mondal, and Chandra (2018)  2018 Rule-based clustering ✗ 
Sun et al. (2017)  2017 Multi-level clustering ✗ 
Lin, Zhu et al. (2017)  2017 Hierarchical clustering with combination of similarity metrics ✗ 
Müller (2017)  2017 Neural network using embeddings ✓ 
Kim, Khabsa, and Giles (2016)  2016 DBSCAN with random forest ✗ 
Momeni and Mayr (2016)  2016 Clustering based on coauthorship ✗ 
Protasiewicz and Dadas (2016)  2016 Rule-based heuristic, linear regression, support vector machines and AdaBoost ✓ 
Qian, Zheng et al. (2015)  2015 Support vector machines ✓ 
Tran, Huynh, and Do (2014)  2014 Deep neural network ✓ 
Caron and van Eck (2014)  2014 Rule-based scoring ✗ 
Schulz, Mazloumian et al. (2014)  2014 Pairwise comparison and clustering ✗ 
Kastner, Choi, and Jung (2013)  2013 Random forest, support vector machines and clustering ✓ 
Wilson (2011)  2011 Single layer perceptron ✓ 
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