Ghent University Academic Bibliography

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- Miscellaneous
- open access
Large language models reflect the ideology of their creators

Maarten Buyl (UGent) , Alexander Rogiers (UGent) , Sander Noels (UGent) , Iris Dominguez-Catena, Edith Heiter (UGent) , Raphaël Romero (UGent) , Iman Johary (UGent) , Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2024) ARXIV.
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- Journal Article
- A1
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An empirical evaluation of network representation learning methods

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

(2023) BIG DATA.
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- PhD Thesis
- open access
Accelerating progress in network representation learning : systematic evaluations and new approaches

Alexandru-Cristian Mara (UGent)

(2023)
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- Journal Article
- A1
- open access
EvalNE : a framework for network embedding evaluation

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

(2022) SOFTWAREX. 17.
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A systematic evaluation of node embedding robustness

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

(2022) PROCEEDINGS OF MACHINE LEARNING RESEARCH. 198.
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- Miscellaneous
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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)
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- 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
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CSNE : conditional signed network embedding

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

(2020) CIKM '20 : PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT. p.1105-1114
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CSNE : Conditional Signed Network Embedding

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

(2020) arXiv.
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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

Large language models reflect the ideology of their creators

An empirical evaluation of network representation learning methods

Accelerating progress in network representation learning : systematic evaluations and new approaches

EvalNE : a framework for network embedding evaluation

A systematic evaluation of node embedding robustness

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

Block-approximated exponential random graphs

CSNE : conditional signed network embedding

CSNE : Conditional Signed Network Embedding

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

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