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François Fouss

Professeur ordinaire
francois.fouss@uclouvain.be

SSH/LSM Louvain School of Management (LSM)

SSH/LRIM Louvain Research Institute in Management and Organizations (LouRIM)

SST/ICTM Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM)

François Fouss is Professor in Information Systems at Louvain School of Management (LSM), Université catholique de Louvain (UCLouvain), in Belgium. He is attached to the Louvain Research Institute in Management and Organizations (LouRIM).
 

His courses focus on information technology and the digital society, from basic concepts to network/graph analysis, through algorithms and programming, data management and data science/analytics - see here for a summary infography (in French).

His research focuses on NTIC and on data analysis, and is developped in various areas such as graph theory, recommender systems, data mining, machine learning, or clustering. His work is twofold, on the one hand in the development of new algorithms, and on the other hand in the analysis of the impact of NTIC.

Year Label School
2007 Docteur en sciences de gestion UCLouvain | Université catholique de Louvain (Belgique)
2002 Diplômé d'études spécialisées en informatique de gestion - Master in Information Systems UCLouvain | Université catholique de Louvain (Belgique)
2001 Ingénieur de gestion UCLouvain | Université catholique de Louvain (Belgique)
1998 Candidat ingénieur de gestion UCLouvain | Université catholique de Louvain (Belgique)

Learning units for 2025

Label Code
Programming and Algorithms MINFO1201
Data Management MINFO1301
Data Analytics MLSMM2116
Web Mining MLSMM2153
Information Technology and Digital Society MQANT1109
2026
Journal article

Timmers, C., Fouss, F., & Vande Kerckhove, C. (2026). PI-adaptDiv: an adaptive algorithm to prevent and escape online filter bubbles. ACM Transactions on Recommender Systems. Accepted/in-press. https://doi.org/10.1145/3803548 (Original work published 2026)

https://research.dial.uclouvain.be/handle/2078.5/273826


2025
Journal article

Satinet, C., Ducarroz, C., & Fouss, F. (2025). Understanding the impact of sustainability-oriented recommender systems on consumers’ choices. Electronic Commerce Research and Applications, 74, 101540. https://doi.org/10.1016/j.elerap.2025.101540 (Original work published 2025)

https://research.dial.uclouvain.be/handle/2078.5/248930


Conference paper

Legast, M., Fouss, F., & Calders, T. (2025). Influence of Label and Selection Bias on Fairness Interventions. Proceedings of Machine Learning Research, 294, 329-334. (Original work published 2025)

https://research.dial.uclouvain.be/handle/2078.5/274839


Satinet, C., Ducarroz, C., & Fouss, F. (2025). Understanding the impact of sustainability-oriented recommender systems on consumers’ choices. EMAC Conference 2025, Madrid.

https://research.dial.uclouvain.be/handle/2078.5/275214


2024
Working paper

Satinet, C., Ducarroz, C., & Fouss, F. (2024). Understanding the impact of sustainability-oriented recommender systems on consumers’ choices (Louvain Research Institute in Management and Organizations Working Paper Series).

https://research.dial.uclouvain.be/handle/2078.5/236312


Journal article

Satinet, C., Fouss, F., Saerens, M., & Leleux, P. (2024). In-processing and post-processing strategies for balancing accuracy and sustainability in product recommendations. Electronic Commerce Research and Applications. Published. https://doi.org/10.1016/j.elerap.2024.101433 (Original work published 2024)

https://research.dial.uclouvain.be/handle/2078.5/247414


2023
Working paper

Satinet, C., Fouss, F., Saerens, M., & Leleux, P. (2023). In-Processing and Post-Processing Strategies for Balancing Accuracy and Sustainability in Product Recommendations (Louvain Research Institute in Management and Organizations Working Paper Series).

https://research.dial.uclouvain.be/handle/2078.5/217240


Conference paper

Satinet, C., Fouss, F., Saerens, M., & Leleux, P. (2023). In-Processing and Post-Processing Strategies for Balancing Accuracy and Sustainability in Product Recommendations. 1st Interdisciplinary Conference on Management, Information Technology and Computer Sciences, Lille, France.

https://research.dial.uclouvain.be/handle/2078.5/217242


2022
Working paper

Satinet, C., & Fouss, F. (2022). A Supervised Machine Learning Classification Framework for Assessing the Sustainability of Clothing Products (Louvain Research Institute in Management and Organizations Working Paper Series).

https://research.dial.uclouvain.be/handle/2078.5/217237


Journal article

Raneri Santo, Lecron Fabian, Hermans, J., & Fouss, F. (2022). Predictions through Lean Startup? Harnessing AI-based predictions under uncertainty. International Journal of Entrepreneurial Behavior & Research. Accepted/in-press. (Original work published 2022)

https://research.dial.uclouvain.be/handle/2078.5/111924


Satinet, C., & Fouss, F. (2022). A Supervised Machine Learning Classification Framework for Clothing Products’ Sustainability. Sustainability, 14(3). https://doi.org/10.3390/su14031334 (Original work published 2022)

https://research.dial.uclouvain.be/handle/2078.5/217239


2021
Conference paper

Vancompernolle Vromman, F., & Fouss, F. (2021). Filter-bubble created by collaborative filtering algorithms themselves, fact or fiction? An experimental comparison. Proceedings of the Data Analytics on Social Media Workshop of the 2021 IEEE/WIC/ACM International Conference on Web Intelligence. Published. Proceedings of the Data Analytics on Social Media Workshop of the 2021 IEEE/WIC/ACM International Conference on Web Intelligence. https://doi.org/10.1145/3498851.3498945 (Original work published 2021)

https://research.dial.uclouvain.be/handle/2078.5/31218


Satinet, C., & Fouss, F. (2021). A Supervised Machine Learning Classification Framework for Clothing Products’ Sustainability. Conférence sur la recherche interdisciplinaire et transdisciplinaire « Transition et Développement durable »., Louvain-la-Neuve.

https://research.dial.uclouvain.be/handle/2078.5/217238


Journal article

Fouss, F., & Fernandes, E. (2021). A closer-to-reality model for comparing relevant dimensions of recommender systems, with application to novelty. Information, 12(12), 500. https://doi.org/10.3390/info12120500 (Original work published 2021)

https://research.dial.uclouvain.be/handle/2078.5/108790


Vandenbulcke Virginie, Ducarroz, C., & Fouss, F. (2021). Collaborative recommendations in the mass retail sector - The role of reactance. Submitted. (Original work published 2021)

https://research.dial.uclouvain.be/handle/2078.5/22698


Working paper

Satinet, C., & Fouss, F. (2021). An aggregated model assessing the risk of job automation – Application to Belgian employment data (Louvain Research Institute in Management and Organizations Working Paper Series 2021/03).

https://research.dial.uclouvain.be/handle/2078.5/113480


2019
Working paper

Fernandes, E., Fouss, F., & Fouss, F. (2019). Adapted Collaborative Filtering Algorithms through Diversity and Novelty.

https://research.dial.uclouvain.be/handle/2078.5/169329


2018
Journal article

Lecron, F., & Fouss, F. (2018). An Optimization Model for Collaborative Recommendation Using a Covariance-Based Regularizer. Data Mining and Knowledge Discovery, 32(3), 651-674. https://doi.org/10.1007/s10618-018-0552-3 (Original work published 2018)

https://research.dial.uclouvain.be/handle/2078.5/171793


2017
Conference paper

Sommer, F., Lecron, F., & Fouss, F. (2017). Recommender systems: the case of repeated interaction in matrix factorization. WI′17 Proceedings of the International Conference on Web Intelligence, p. 843-847. https://doi.org/10.1145/3106426.3106522

https://research.dial.uclouvain.be/handle/2078.5/226720


Vandenbulcke, V., Ducarroz, C., & Fouss, F. (2017). Recommandations collaboratives personnalisées : Quel impact sur le comportement du consommateur en grande distribution ? 33ème Congrès International de l’AFM (Association Française du Marketing), Tours, France.

https://research.dial.uclouvain.be/handle/2078.5/226897


Vandenbulcke, V., Ducarroz, C., & Fouss, F. (2017). Personalized Collaborative Recommendations in the Mass-retailing Sector: the Impact of the Recommended Products and the Accompanying Message on Consumer Behavior. EMAC (European Marketing Academy) - 46th Annual Conference, Groningen (Netherlands).

https://research.dial.uclouvain.be/handle/2078.5/227365


Working paper

vandenbulcke virginie, Ducarroz, C., & Fouss, F. (2017). Personalized Collaborative recommenations in the Mass-retailing Sector: the Impact of the Recommended Products and the Accompanying Message on Consume Behavior (Working Paper LSM 2017/16).

https://research.dial.uclouvain.be/handle/2078.5/169327


Sommer, F., Fouss, F., & Saerens, M. (2017). Modularity-driven kernel k-means for community detection (Louvain Research Institute in Management and Organizations Working Paper Series 2017/21).

https://research.dial.uclouvain.be/handle/2078.5/173411


Journal article

Sommer, F., Fouss, F., & Saerens, M. (2017). Modularity-driven kernel k-means for community detection. Lecture Notes in Computer Science, 10614, 423-433. https://doi.org/10.1007/978-3-319-68612-7_48 (Original work published 2017)

https://research.dial.uclouvain.be/handle/2078.5/172783


2016
Monography

Fouss, F., Saerens, M., & Shimbo, M. (2016). Algorithms and Models for Network Data and Link Analysis. Cambridge University Press.

https://research.dial.uclouvain.be/handle/2078.5/184219


Journal article

Sommer, F., Fouss, F., & Saerens, M. (2016). Comparison of Graph Node Distances on Clustering Tasks. Lecture Notes in Computer Science, 9886, 192-201. https://doi.org/10.1007/978-3-319-44778-0_23 (Original work published 2016)

https://research.dial.uclouvain.be/handle/2078.5/176550


2015
Conference paper

Vandenbulcke, V., Ducarroz, C., & Fouss, F. (2015). Evaluating the impact of personalized recommendations: Application in the mass-retailing sector. 44th European Marketing Academy (EMAC) Conference, Leuven (Belgium).

https://research.dial.uclouvain.be/handle/2078.5/230072


Working paper

Sommer, F., Fouss, F., & Saerens, M. (2015). Clustering using a Sum-Over-Forests weighted kernel k-means approach (Louvain School of Management Working Paper Series 2015/22).

https://research.dial.uclouvain.be/handle/2078.5/127599


Journal article

Fouss, F. (2015). Le Big Data est à nous! La Libre. Published. (Original work published 2015)

https://research.dial.uclouvain.be/handle/2078.5/124841


2014
Working paper

vandenbulcke virginie, Ducarroz, C., & Fouss, F. (2014). Evaluationg the impact of personalized recommendations : Application in the mass-retailing sector (Working Paper LSM 2014/22).

https://research.dial.uclouvain.be/handle/2078.5/124838


Sommer, F., & Fouss, F. (2014). Learning with product graphs and multiple labels (Working Paper LSM 2014/20).

https://research.dial.uclouvain.be/handle/2078.5/124836


Conference paper

Senelle, M., Saerens, M., & Fouss, F. (2014). The Sum-over-Forests clustering. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Published. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges.

https://research.dial.uclouvain.be/handle/2078.5/231905


Van Parijs, C., & Fouss, F. (2014). Improving accuracy by reducing the importance of hubs in nearest-neighbor recommendations. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges.

https://research.dial.uclouvain.be/handle/2078.5/231832


Journal article

Senelle, M., Garcia Diez, S., Mantrach, A., Shimbo, M., Saerens, M., & Fouss, F. (2014). The Sum-over-Forests density index: identifying dense regions in a graph. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6), 1268-1274. https://doi.org/10.1109/TPAMI.2013.227 (Original work published 2014)

https://research.dial.uclouvain.be/handle/2078.5/202738


2013
Conference paper

Vandenbulcke, V., Lecron, F., Ducarroz, C., & Fouss, F. (2013). Customer segmentation based on a collaborative recommendation system: application to a retail company. Proceedings of the 2013 conference of European Marketing Academy. Published. Conference of European Marketing Academy (EMAC), Istanbul.

https://research.dial.uclouvain.be/handle/2078.5/231908


2012
Journal article

Françoisse, K., Fouss, F., & Saerens, M. (2012). A Link-Analysis-Based Discriminant Analysis for Exploring Partially Labeled Graphs. Pattern Recognition Letters, 34(2), 146-154. https://doi.org/10.1016/j.patrec.2012.07.025 (Original work published 2013)

https://research.dial.uclouvain.be/handle/2078.5/207916


Fouss, F., Françoisse, K., Yen, L., Pirotte, A., & Saerens, M. (2012). An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Networks, 31, 53-72. https://doi.org/10.1016/j.neunet.2012.03.001 (Original work published 2012)

https://research.dial.uclouvain.be/handle/2078.5/207934


2011
Journal article

Garcia Diez, S., Fouss, F., Shimbo, M., & Saerens, M. (2011). A sum-over-paths extension of edit distances accounting for all sequence alignments. Pattern Recognition, 44(6), 1172-1182. https://doi.org/10.1016/j.patcog.2010.11.020 (Original work published 2011)

https://research.dial.uclouvain.be/handle/2078.5/160693


Yen, L., Saerens, M., & Fouss, F. (2011). A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases. IEEE Transactions on Knowledge & Data Engineering, 23(4), 481-495. https://doi.org/10.1109/TKDE.2010.142 (Original work published 2011)

https://research.dial.uclouvain.be/handle/2078.5/160689


Conference paper

Garcia Diez, S., Saerens, M., Senelle, M., & Fouss, F. (2011). A Simple-cycles weighted kernel based on harmony structure for similarity retrieval. Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), Miami, Florida, USA.

https://research.dial.uclouvain.be/handle/2078.5/225267


2010
Book chapter

Fouss, F. (2010). Collaborative-recommendation systems and link analysis. In Pascal Francq (ed.), Collaborative Search and Communities of Interest: Trends in Knowledge Sharing and Assessment.

https://research.dial.uclouvain.be/handle/2078.5/218562


Fouss, F. (2010). Introduction to recommender systems. In Pascal Francq (ed.), Collaborative search and communities of interest [electronic resource] : trends in knowledge sharing and assessment. https://doi.org/10.4018/978-1-61520-841-8

https://research.dial.uclouvain.be/handle/2078.5/195206


Journal article

Fouss, F., Achbany, Y., & Saerens, M. (2010). A probabilistic reputation model based on transaction ratings. Information Sciences, 180(11), 2095-2123. https://doi.org/10.1016/j.ins.2010.01.020 (Original work published 2010)

https://research.dial.uclouvain.be/handle/2078.5/250330


Conference paper

Garcia Diez, S., Fouss, F., Shimbo, M., & Saerens, M. (2010). Normalized Sum-over-Paths Edit Distances. International Conference on Pattern Recognition, Istanbul, Turkey.

https://research.dial.uclouvain.be/handle/2078.5/230905


2009
Journal article

Yen, L., Fouss, F., Decaestecker, C., Francq, P., & Saerens, M. (2009). Graph nodes clustering with the sigmoid commute-time kernel: A comparative study. Data & Knowledge Engineering, 68(3), 338-361. https://doi.org/10.1016/j.datak.2008.10.006 (Original work published 2009)

https://research.dial.uclouvain.be/handle/2078.5/250373


Saerens, M., Fouss, F., Achbany, Y., & Yen, L. (2009). Randomized shortest-path problems: Two related models. Neural Computation, 21(8), 2363-2404. https://doi.org/10.1162/neco.2009.11-07-643 (Original work published 2009)

https://research.dial.uclouvain.be/handle/2078.5/250418


2008
Journal article

Achbany, Y., Jureta, I., Faulkner, S., & Fouss, F. (2008). Continually Learning Optimal Web Service Compositions. IEEE Transactions on Services Computing, 1, 141-154. https://doi.org/10.1109/TSC.2008.12 (Original work published 2008)

https://research.dial.uclouvain.be/handle/2078.5/249513


Achbany, Y., Fouss, F., Yen, L., Pirotte, A., & Saerens, M. (2008). Tuning continual exploration in reinforcement learning: An optimality property of the Boltzmann strategy. Neurocomputing, 71(13-15), 2507-2520. https://doi.org/10.1016/j.neucom.2007.11.040 (Original work published 2008)

https://research.dial.uclouvain.be/handle/2078.5/133508


Working paper

Fouss, F., Achbany, Y., & Saerens, M. (2008). A probabilistic reputation model (IAG - LSM Working Papers 08/20).

https://research.dial.uclouvain.be/handle/2078.5/249681


Conference paper

Herssens, C., Faulkner, S., Fouss, F., & Jureta, I. (2008). A Framework for QoS Driven Selection of Services. IEEE International Conference on Services Computing, Honolulu, Hawaii, USA.

https://research.dial.uclouvain.be/handle/2078.5/250465


Fouss, F., & Saerens, M. (2008). Evaluating performance of recommender systems: An experimental comparison. IEEE/WIC/ACM International Conference on Web Intelligence, Sydney, Australia.

https://research.dial.uclouvain.be/handle/2078.5/250215


2007
Journal article

Yen, L., Saerens, M., Francq, P., Decaestecker, C., & Fouss, F. (2007). Graph Nodes Clustering based on the Commute-Time Kernel. Lecture Notes in Computer Science, 4426, 1037-1045. https://doi.org/10.1007/978-3-540-71701-0_117 (Original work published 2007)

https://research.dial.uclouvain.be/handle/2078.5/128495


Fouss, F., Pirotte, A., Saerens, M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph, with application to collaborative recommendation. IEEE Transactions on Knowledge & Data Engineering, 19(3), 355-369. https://doi.org/10.1109/TKDE.2007.46 (Original work published 2007)

https://research.dial.uclouvain.be/handle/2078.5/250067


Dissertation

Fouss, F. (2007). Measures of similarity on graphs : Investigation and application to collaborative recommendation.

https://research.dial.uclouvain.be/handle/2078.5/129925


2006
Conference paper

Fouss, F., Yen, L., Pirotte, A., & Saerens, M. (2006). An experimental investigation of graph kernels on a collaborative recommendation task. IEEE International Conference on Data Mining (ICDM 2006), Hong Kong, China.

https://research.dial.uclouvain.be/handle/2078.5/223372


Achbany, Y., Fouss, F., Yen, L., Pirotte, A., & Saerens, M. (2006). Optimal tuning of continual, online, exploration in reinforcement learning. Lecture Notes in Computer Science, Vol. 4131, p. 790-800 (2006).

https://research.dial.uclouvain.be/handle/2078.5/250408


Working paper

Fouss, F., Pirotte, A., Saerens, M., Renders, J.-M., & Yen, L. (2006). A novel way of computing similarities between nodes of a graph, with application to collaborative filtering and subspace projection of the graph nodes (IAG - LSM Working Papers 06/08).

https://research.dial.uclouvain.be/handle/2078.5/249580


Journal article

Achbany, Y., Saerens, M., Pirotte, A., Yen, L., & Fouss, F. (2006). Optimal Tuning of Continual Online Exploration in Reinforcement Learning. Lecture Notes in Computer Science, 4131. (Original work published 2006)

https://research.dial.uclouvain.be/handle/2078.5/128494


2005
Working paper

Saerens, M., & Fouss, F. (2005). Hits is PCA (IAG Working Papers 2005/125).

https://research.dial.uclouvain.be/handle/2078.5/130016


Fouss, F., Faulkner, S., Kolp, M., Pirotte, A., & Saerens, M. (2005). Web recommendation system based on a markov-chain model (IAG Working Papers 2005/123).

https://research.dial.uclouvain.be/handle/2078.5/130011


Saerens, M., Fouss, F., Yen, L., & Dupont, P. (2005). The principal components analysis of a graph and its relationships to spectral clustering (IAG Working Papers 2005/124).

https://research.dial.uclouvain.be/handle/2078.5/130013


Conference paper

Fouss, F., Saerens, M., Pirotte, A., Kolp, M., & Faulkner, S. (2005). Web recommendation system based on a Markov-chain model. International Conference on Enterprise Information Systems (ICEIS 2005), Miami, USA.

https://research.dial.uclouvain.be/handle/2078.5/223369


Fouss, F., Renders, J.-M., Pirotte, A., & Saerens, M. (2005). A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation. IEEE WIC/ACM International Joint Conference on Web Intelligence, Compiègne, France.

https://research.dial.uclouvain.be/handle/2078.5/223376


Saerens, M., & Fouss, F. (2005). HITS is principal components analysis. 2005 IEEE/ACM International Joint Conference on Web Intelligence.

https://research.dial.uclouvain.be/handle/2078.5/250756


Yen, L., VanVyve, D. J., Wouters, F., Fouss, F., Verleysen, M., & Saerens, M. (2005). Clustering using a random walk-based distance measure. Proceedings of the 13th European Symposium on Artificial Neural Networks, p. 317-324.

https://research.dial.uclouvain.be/handle/2078.5/253894


2004
Conference paper

Saerens, M., Fouss, F., Dupont, P., & Pirotte, A. (2004). Collaborative filtering based on random walks on a graph. Workshop on Large Networks, UCL, LLN.

https://research.dial.uclouvain.be/handle/2078.5/225950


Fouss, F., Pirotte, A., & Saerens, M. (2004). A Novel Way of Computing Dissimilarities between Nodes of a Graph, with Application to Collaborative Filtering. Proceedings of the Workshop on Statistical Approaches for Web Mining.

https://research.dial.uclouvain.be/handle/2078.5/250385


Fouss, F., Renders, J.-M., & Saerens, M. (2004). Some relationships between between Kleinberg’s hubs and authorities, correspondence analysis and Markov chains. 7th International Conference on the Statistical Analysis of Textual Data.

https://research.dial.uclouvain.be/handle/2078.5/250697


Saerens, M., Fouss, F., Yen, L., & Dupont, P. (2004). The principle components analysis of a graph, and its relationships to spectral clustering. In Boulicaut, J.-F.; Esposito, F.; Giannotti, F.; Pedreschi, D.; (ed.), Machine Learning: ECML 2004. 15th European Conference on MachineLearning. Proceedings (Lecture Notes in Artificial IntelligenceVol.3201) (p. p. 371-383). Springer-verlag.

https://research.dial.uclouvain.be/handle/2078.5/231211


Saerens, M., & Fouss, F. (2004). Yet another method for combining experts opinions. 5th International Workshop on Multiple Classifier Systems.

https://research.dial.uclouvain.be/handle/2078.5/250733


Fouss, F., Renders, J.-M., & Saerens, M. (2004). Some relationships between Kleinberg’s hubs and authorities, correspondence analysis, and the Salsa algorithm. International Conference on the Statistical Analysis of Textual Data (JADT 2004), Louvain-la-Neuve, Belgium.

https://research.dial.uclouvain.be/handle/2078.5/223357


Fouss, F., Pirotte, A., & Saerens, M. (2004). The Application of New Concepts of Dissimilarities between Nodes of a Graph to Collaborative Filtering. Workshop on Statistical Approaches for Web Mining (SAWM), Pisa, Italy.

https://research.dial.uclouvain.be/handle/2078.5/223352


Saerens, M., Fouss, F., Yen, L., & Dupont, P. (2004). The principal components analysis of a graph, and its relationships to spectral clustering. Lecture Notes in Computer Science, 3201, 371-383. (Original work published 2004)

https://research.dial.uclouvain.be/handle/2078.5/26066


Journal article

Fouss, F., & Saerens, M. (2004). Yet another method for combining classifiers outputs: A maximum entropy approach. Lecture Notes in Computer Science, 3077. (Original work published 2004)

https://research.dial.uclouvain.be/handle/2078.5/128496


Working paper

Fouss, F., & Saerens, M. (2004). A maximum entropy approach to multiple classifiers combination (IAG - LSM Working Papers 04/107).

https://research.dial.uclouvain.be/handle/2078.5/249737


2003
Working paper

Donnay, A., Fouss, F., Kolp, M., Massart, D., & Pirotte, A. (2003). Analyse oriente objet de processus sidérurgiques de type cokier (IAG - LSM Working Papers 03/86).

https://research.dial.uclouvain.be/handle/2078.5/250849


Fouss, F., Renders, J.-M., & Saerens, M. (2003). Links between Kleinberg’s hubs and authorities, correspondence analysis, and Markov chains (ECON Discussion Papers 2003/101).

https://research.dial.uclouvain.be/handle/2078.5/129979


Donnay, A., Fouss, F., Kolp, M., Massart, D., & Pirotte, A. (2003). Analyse orientée objet de processus sidérurgiques de type cokier (IAG Working Papers 2003/86).

https://research.dial.uclouvain.be/handle/2078.5/129988


Fouss, F., Ibarz, M., Kolp, M., & Pirotte, A. (2003). Steel production data warehouse reengineering (ECON Discussion Papers 2003/89).

https://research.dial.uclouvain.be/handle/2078.5/129987


Conference paper

Fouss, F., Renders, J.-M., & Saerens, M. (2003). Links between Kleinberg’s hubs and authorities, correspondence analysis and Markov chains. IEEE International Conference on Data Mining (ICDM 2003), Melbourne, USA.

https://research.dial.uclouvain.be/handle/2078.5/223359