| Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation F Fouss, A Pirotte, JM Renders, M Saerens IEEE Transactions on knowledge and data engineering 19 (3), 355-369, 2007 | 1642 | 2007 |
| The principal components analysis of a graph, and its relationships to spectral clustering M Saerens, F Fouss, L Yen, P Dupont Machine Learning: ECML 2004: 15th European Conference on Machine Learning …, 2004 | 341 | 2004 |
| An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification F Fouss, K Francoisse, L Yen, A Pirotte, M Saerens Neural networks 31, 53-72, 2012 | 187 | 2012 |
| A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation F Fouss, A Pirotte, M Saerens The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05 …, 2005 | 152 | 2005 |
| An experimental investigation of graph kernels on a collaborative recommendation task F Fouss, L Yen, A Pirotte, M Saerens Sixth International Conference on Data Mining (ICDM'06), 863-868, 2006 | 149 | 2006 |
| Algorithms and models for network data and link analysis F Fouss, M Saerens, M Shimbo Cambridge University Press, 2016 | 139 | 2016 |
| clustering using a random walk based distance measure. L Yen, D Vanvyve, F Wouters, F Fouss, M Verleysen, M Saerens ESANN, 317-324, 2005 | 139 | 2005 |
| Randomized shortest-path problems: Two related models M Saerens, Y Achbany, F Fouss, L Yen Neural computation 21 (8), 2363-2404, 2009 | 133 | 2009 |
| Graph nodes clustering with the sigmoid commute-time kernel: A comparative study L Yen, F Fouss, C Decaestecker, P Francq, M Saerens Data & Knowledge Engineering 68 (3), 338-361, 2009 | 113 | 2009 |
| Graph nodes clustering based on the commute-time kernel L Yen, F Fouss, C Decaestecker, P Francq, M Saerens Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1037-1045, 2007 | 108 | 2007 |
| Evaluating performance of recommender systems: An experimental comparison F Fouss, M Saerens 2008 IEEE/WIC/ACM International Conference on Web Intelligence and …, 2008 | 63 | 2008 |
| A probabilistic reputation model based on transaction ratings F Fouss, Y Achbany, M Saerens Information Sciences 180 (11), 2095-2123, 2010 | 57 | 2010 |
| Optimal tuning of continual online exploration in reinforcement learning Y Achbany, F Fouss, L Yen, A Pirotte, M Saerens Artificial Neural Networks–ICANN 2006: 16th International Conference, Athens …, 2006 | 39 | 2006 |
| Yet another method for combining classifiers outputs: a maximum entropy approach M Saerens, F Fouss International Workshop on Multiple Classifier Systems, 82-91, 2004 | 39 | 2004 |
| Tuning continual exploration in reinforcement learning: An optimality property of the Boltzmann strategy Y Achbany, F Fouss, L Yen, A Pirotte, M Saerens Neurocomputing 71 (13-15), 2507-2520, 2008 | 33 | 2008 |
| Comparison of graph node distances on clustering tasks F Sommer, F Fouss, M Saerens Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016 | 32 | 2016 |
| Continually learning optimal allocations of services to tasks Y Achbany, IJ Jureta, S Faulkner, F Fouss IEEE Transactions on Services Computing 1 (3), 141-154, 2008 | 26 | 2008 |
| A sum-over-paths extension of edit distances accounting for all sequence alignments S García-Díez, F Fouss, M Shimbo, M Saerens Pattern Recognition 44 (6), 1172-1182, 2011 | 25 | 2011 |
| A link analysis extension of correspondence analysis for mining relational databases L Yen, M Saerens, F Fouss IEEE Transactions on Knowledge and Data Engineering 23 (4), 481-495, 2010 | 23 | 2010 |
| Tuning continual exploration in reinforcement learning Y Achbany, F Fouss, L Yen, A Pirotte, M Saerens Technical report3, 2005 | 17 | 2005 |
