Ghent University Academic Bibliography

Your filters: cql: keyword exact "link prediction"

Add to list
- Journal Article
- A1
- open access
An empirical evaluation of network representation learning methods

Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2024) BIG DATA. 12(6). p.518-537
Add to list
- Journal Article
- A1
- open access
Exploring the performance of continuous-time dynamic link prediction algorithms

Raphaël Romero (UGent) , Maarten Buyl (UGent) , Tijl De Bie (UGent) and Jefrey Lijffijt (UGent)

(2024) APPLIED SCIENCES-BASEL. 14(8).
Add to list
- Conference Paper
- P1
- open access
The KL-divergence between a graph model and its fair I-projection as a fairness regularizer

Maarten Buyl (UGent) and Tijl De Bie (UGent)

(2021) MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021 : RESEARCH TRACK, PT II. In Lecture Notes in Artificial Intelligence 12976. p.351-366
Add to list
- Journal Article
- A1
- open access
ALPINE : active link prediction using network embedding

Xi Chen, Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2021) APPLIED SCIENCES-BASEL. 11(11).
Add to list
- Miscellaneous
- open access
The KL-divergence between a graph model and its fair I-projection as a fairness regularizer

Maarten Buyl (UGent) and Tijl De Bie (UGent)

(2021) arXiv.
Add to list
Adversarial robustness of probabilistic network embedding for link prediction

Xi Chen, Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2021) ECML PKDD 2021 Workshops, Proceedings, Part II. In Communications in Computer and Information Science 1525. p.22-38
Add to list
- Conference Paper
- P1
- open access
Explanations for network embedding-based link predictions

Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2021) MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021, PT I. In Communications in Computer and Information Science 1524. p.473-488
Add to list
- Conference Paper
- P1
- open access
Block-approximated exponential random graphs

Florian Adriaens, Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020) 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020). In Proceedings of the International Conference on Data Science and Advanced Analytics p.70-80
Add to list
- Miscellaneous
- open access
Network representation learning for link prediction : are we improving upon simple heuristics?

Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020)
Add to list
- Conference Paper
- P1
- open access
Benchmarking network embedding models for link prediction : are we making progress?

Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020) 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020). In Proceedings of the International Conference on Data Science and Advanced Analytics p.138-147

Academic Bibliography

An empirical evaluation of network representation learning methods

Exploring the performance of continuous-time dynamic link prediction algorithms

The KL-divergence between a graph model and its fair I-projection as a fairness regularizer

ALPINE : active link prediction using network embedding

The KL-divergence between a graph model and its fair I-projection as a fairness regularizer

Adversarial robustness of probabilistic network embedding for link prediction

Explanations for network embedding-based link predictions

Block-approximated exponential random graphs

Network representation learning for link prediction : are we improving upon simple heuristics?

Benchmarking network embedding models for link prediction : are we making progress?

Contents

Support

Contact