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Alexandru Cristian Mara
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Year
Semi-supervised learning in network-structured data via total variation minimization
A Jung, AO Hero III, AC Mara, S Jahromi, A Heimowitz, YC Eldar
IEEE Transactions on Signal Processing 67 (24), 6256-6269, 2019
402019
When is network lasso accurate?
A Jung, N Tran, A Mara
Frontiers in Applied Mathematics and Statistics 3, 28, 2018
372018
Benchmarking network embedding models for link prediction: Are we making progress?
AC Mara, J Lijffijt, T De Bie
2020 IEEE 7th International conference on data science and advanced …, 2020
292020
Evalne: A framework for evaluating network embeddings on link prediction
A Mara, J Lijffijt, T De Bie
arXiv preprint arXiv:1901.09691, 2019
262019
Semi-supervised learning via sparse label propagation
A Jung, AO Hero III, A Mara, S Jahromi
arXiv preprint arXiv:1612.01414, 2016
262016
CLUS: parallel subspace clustering algorithm on spark
B Zhu, A Mara, A Mozo
East European Conference on Advances in Databases and Information Systems …, 2015
182015
CSNE: Conditional signed network embedding
A Mara, Y Mashayekhi, J Lijffijt, T De Bie
Proceedings of the 29th ACM international conference on information …, 2020
172020
Recovery conditions and sampling strategies for network lasso
A Mara, A Jung
2017 51st Asilomar Conference on Signals, Systems, and Computers, 405-409, 2017
62017
Scalable semi-supervised learning over networks using nonsmooth convex optimization
A Jung, AO Hero III, A Mara, S Aridhi
arXiv preprint arXiv:1611.00714, 2016
62016
EvalNE: A framework for network embedding evaluation
A Mara, J Lijffijt, T De Bie
SoftwareX 17, 100997, 2022
52022
Network representation learning for link prediction: Are we improving upon simple heuristics?
AC Mara, J Lijffijt, T De Bie
52020
Block-approximated exponential random graphs
F Adriaens, A Mara, J Lijffijt, T De Bie
2020 IEEE 7th International Conference on Data Science and Advanced …, 2020
42020
When is network lasso accurate?
A Jung, NT Quang, A Mara
arXiv preprint arXiv:1704.02107, 2017
42017
Reproducible Evaluations of Network Representation Learning Models Using EvalNE
A Mara, J Lijffijt, T de Bie
32021
An empirical evaluation of network representation learning methods
AC Mara, J Lijffijt, T De Bie
Big Data, 2022
22022
Scalable dyadic independence models with local and global constraints
F Adriaens, A Mara, J Lijffijt, T De Bie
arXiv preprint cs.SI/2002.07076, 2020
22020
A systematic evaluation of node embedding robustness
AC Mara, J Lijffijt, S Günnemann, T De Bie
Learning on Graphs Conference, 42: 1-42: 14, 2022
12022
A Comparative Analysis of Graph Signal Recovery Methods for Big Data Networks
A Mara
12017
Accelerating progress in network representation learning: systematic evaluations and new approaches
AC Mara
Ghent University, 2023
2023
Big Data Network Traffic Summary Dataset
E Fernández, A Mara, Á Martínez, A Mozo, B Ordozgoiti, S Vakaruk, B Zhu, ...
2015
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Articles 1–20