Advanced search
1 file | 440.19 KB Add to list

ALPINE : Active Link Prediction using Network Embedding

Xi Chen (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)
(2020) arXiv.
Author
Organization
Abstract
Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm. Unfortunately, such queries can be expensive or time-consuming, mandating the careful consideration of which node pairs to query. In this paper we estimate the improvement in link prediction accuracy after querying any particular node pair, to use in an active learning setup. Specifically, we propose ALPINE (Active Link Prediction usIng Network Embedding), the first method to achieve this for link prediction based on network embedding. To this end, we generalized the notion of V-optimality from experimental design to this setting, as well as more basic active learning heuristics originally developed in standard classification settings. Empirical results on real data show that ALPINE is scalable, and boosts link prediction accuracy with far fewer queries.

Downloads

  • 2002.01227.pdf
    • full text (Author's original)
    • |
    • open access
    • |
    • PDF
    • |
    • 440.19 KB

Citation

Please use this url to cite or link to this publication:

MLA
Chen, Xi, et al. “ALPINE : Active Link Prediction Using Network Embedding.” ArXiv, 2020.
APA
Chen, X., Kang, B., Lijffijt, J., & De Bie, T. (2020). ALPINE : Active Link Prediction using Network Embedding. arXiv.
Chicago author-date
Chen, Xi, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2020. “ALPINE : Active Link Prediction Using Network Embedding.” ArXiv.
Chicago author-date (all authors)
Chen, Xi, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2020. “ALPINE : Active Link Prediction Using Network Embedding.” ArXiv.
Vancouver
1.
Chen X, Kang B, Lijffijt J, De Bie T. ALPINE : Active Link Prediction using Network Embedding. arXiv. 2020.
IEEE
[1]
X. Chen, B. Kang, J. Lijffijt, and T. De Bie, “ALPINE : Active Link Prediction using Network Embedding,” arXiv. 2020.
@misc{8649295,
  abstract     = {Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network.
Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm. Unfortunately, such queries can be expensive or time-consuming, mandating the careful consideration of which node pairs to query. In this paper we estimate the improvement in link prediction accuracy after querying any particular node pair, to use in an active learning setup. 
Specifically, we propose ALPINE (Active Link Prediction usIng Network Embedding), the first method to achieve this for link prediction based on network embedding. To this end, we generalized the notion of V-optimality from experimental design to this setting, as well as more basic active learning heuristics originally developed in standard classification settings. Empirical results on real data show that ALPINE is scalable, and boosts link prediction accuracy with far fewer queries.},
  author       = {Chen, Xi and Kang, Bo and Lijffijt, Jefrey and De Bie, Tijl},
  language     = {eng},
  pages        = {8},
  series       = {arXiv},
  title        = {ALPINE : Active Link Prediction using Network Embedding},
  url          = {https://arxiv.org/abs/2002.01227},
  year         = {2020},
}