FONDUE : framework for node disambiguation using network embeddings
- Author
- Arno Mel (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)
- Organization
- Project
- Abstract
- Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest form, networks represent real-life entities (e.g. people, papers, proteins, concepts) as nodes, and describe them in terms of their relations with other entities by means of edges between these nodes. This can be valuable for a range of purposes from the study of information diffusion to bibliographic analysis, bioinformatics research, and question-answering.The quality of networks is often problematic though, affecting downstream tasks. This paper focuses on the common problem where a node in the network in fact corresponds to multiple real-life entities. In particular, we introduce FONDUE, an algorithm based on network embedding for node disambiguation. Given a network, FONDUE identifies nodes that correspond to multiple entities, for subsequent splitting. Extensive experiments on fourteen benchmark datasets demonstrate that FONDUE is substantially and uniformly more accurate for ambiguous node identification compared to the existing state-of-the-art with a lower computational cost, while less optimal for determining the best way to split ambiguous nodes.
- Keywords
- node disambiguation, network embeddings, representation learning, SEMIDEFINITE RELAXATION
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8678463
- MLA
- Mel, Arno, et al. “FONDUE : Framework for Node Disambiguation Using Network Embeddings.” 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), edited by G. Webb et al., IEEE, 2020, pp. 158–67, doi:10.1109/DSAA49011.2020.00028.
- APA
- Mel, A., Kang, B., Lijffijt, J., & De Bie, T. (2020). FONDUE : framework for node disambiguation using network embeddings. In G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos, & L. Cao (Eds.), 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020) (pp. 158–167). https://doi.org/10.1109/DSAA49011.2020.00028
- Chicago author-date
- Mel, Arno, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2020. “FONDUE : Framework for Node Disambiguation Using Network Embeddings.” In 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), edited by G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos, and L. Cao, 158–67. IEEE. https://doi.org/10.1109/DSAA49011.2020.00028.
- Chicago author-date (all authors)
- Mel, Arno, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2020. “FONDUE : Framework for Node Disambiguation Using Network Embeddings.” In 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), ed by. G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos, and L. Cao, 158–167. IEEE. doi:10.1109/DSAA49011.2020.00028.
- Vancouver
- 1.Mel A, Kang B, Lijffijt J, De Bie T. FONDUE : framework for node disambiguation using network embeddings. In: Webb G, Zhang Z, Tseng VS, Williams G, Vlachos M, Cao L, editors. 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020). IEEE; 2020. p. 158–67.
- IEEE
- [1]A. Mel, B. Kang, J. Lijffijt, and T. De Bie, “FONDUE : framework for node disambiguation using network embeddings,” in 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), Sydney, Australia (online), 2020, pp. 158–167.
@inproceedings{8678463,
abstract = {{Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest form, networks represent real-life entities (e.g. people, papers, proteins, concepts) as nodes, and describe them in terms of their relations with other entities by means of edges between these nodes. This can be valuable for a range of purposes from the study of information diffusion to bibliographic analysis, bioinformatics research, and question-answering.The quality of networks is often problematic though, affecting downstream tasks. This paper focuses on the common problem where a node in the network in fact corresponds to multiple real-life entities. In particular, we introduce FONDUE, an algorithm based on network embedding for node disambiguation. Given a network, FONDUE identifies nodes that correspond to multiple entities, for subsequent splitting. Extensive experiments on fourteen benchmark datasets demonstrate that FONDUE is substantially and uniformly more accurate for ambiguous node identification compared to the existing state-of-the-art with a lower computational cost, while less optimal for determining the best way to split ambiguous nodes.}},
author = {{Mel, Arno and Kang, Bo and Lijffijt, Jefrey and De Bie, Tijl}},
booktitle = {{2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020)}},
editor = {{Webb, G. and Zhang, Z. and Tseng, V. S. and Williams, G. and Vlachos, M. and Cao, L.}},
isbn = {{9781728182063}},
issn = {{2472-1573}},
keywords = {{node disambiguation,network embeddings,representation learning,SEMIDEFINITE RELAXATION}},
language = {{eng}},
location = {{Sydney, Australia (online)}},
pages = {{158--167}},
publisher = {{IEEE}},
title = {{FONDUE : framework for node disambiguation using network embeddings}},
url = {{http://doi.org/10.1109/DSAA49011.2020.00028}},
year = {{2020}},
}
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