| When is network lasso accurate? A Jung, N Tran, A Mara Frontiers in Applied Mathematics and Statistics 3, 28, 2018 | 36 | 2018 |
| 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 | 34 | 2019 |
| Evalne: A framework for evaluating network embeddings on link prediction A Mara, J Lijffijt, T De Bie arXiv preprint arXiv:1901.09691, 2019 | 24 | 2019 |
| 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 | 22 | 2020 |
| Semi-supervised learning via sparse label propagation A Jung, AO Hero III, A Mara, S Jahromi arXiv preprint arXiv:1612.01414, 2016 | 22 | 2016 |
| CLUS: parallel subspace clustering algorithm on spark B Zhu, A Mara, A Mozo New Trends in Databases and Information Systems: ADBIS 2015 Short Papers and …, 2015 | 17 | 2015 |
| CSNE: Conditional signed network embedding A Mara, Y Mashayekhi, J Lijffijt, T De Bie Proceedings of the 29th ACM international conference on information …, 2020 | 13 | 2020 |
| Network Representation Learning for Link Prediction: Are we improving upon simple heuristics? AC Mara, J Lijffijt, T De Bie | 5 | 2020 |
| 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 | 5 | 2016 |
| Recovery conditions and sampling strategies for network lasso A Mara, A Jung 2017 51st Asilomar Conference on Signals, Systems, and Computers, 405-409, 2017 | 4 | 2017 |
| EvalNE: A framework for network embedding evaluation A Mara, J Lijffijt, T De Bie SoftwareX 17, 100997, 2022 | 3 | 2022 |
| Reproducible Evaluations of Network Representation Learning Models Using EvalNE A Mara, J Lijffijt, T de Bie | 3 | 2021 |
| An empirical evaluation of network representation learning methods AC Mara, J Lijffijt, T De Bie Big Data, 2022 | 2 | 2022 |
| 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 | 2 | 2020 |
| 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 | 2 | 2020 |
| A Comparative Analysis of Graph Signal Recovery Methods for Big Data Networks A Mara | 1 | 2017 |
| Accelerating progress in network representation learning: systematic evaluations and new approaches AC Mara Ghent University, 2023 | | 2023 |
| 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 | | 2022 |
| Big Data Network Traffic Summary Dataset E Fernández, A Mara, Á Martínez, A Mozo, B Ordozgoiti, S Vakaruk, B Zhu, ... | | 2015 |
| ONTIC D1. 1: Deliverable Planning for the Next Period# 1 A Mozo, A Mara, B Zhu, S Gómez, P Owezarski, A Bascuñana, P Sánchez, ... Universidad politécnica de Madrid; CNRS-LAAS; Ericsson Spain; SATEC …, 2015 | | 2015 |
Tijl De BieFull Professor, Ghent UniversityVerified email at ugent.be
