Journal Articles

Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2019). Tcc2vec: RFM-Informed Representation Learning on Call Graphs for Churn Prediction. Information Sciences, Applications, Information Sciences, Applications; 2019.

Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2018). On the operational efficiency of different feature types for telco churn prediction. European Journal of Operational Research, 267(3), 1141-1155.

Sesar, Branimir, Hernitschek, Nina, Mitrovic, Sandra, Ivezic, Zeljko, Rix, Hans-Walter, Cohen, Judith G, . . . Waters, Christopher. (2017). Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample. Astronomical Journal, 153(5), Astronomical Journal; 2017; Vol. 153; iss. 5; pp.

Conference Proceedings

Mitrovic, Sandra, & De Weerdt, Jochen. (2019). Dyn2Vec: Exploiting dynamic behaviour using difference networks-based node embeddings for classification. Proceedings of the International Conference on Data Science, 194-200.

Mitrovic, Sandra, & De Weerdt, Jochen. (2019). Probabilistic Random Walks for Churn Prediction using Representation Learning. Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG), in Conjunction with KDD 2018., Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG), in conjunction with KDD 2018.; 2019.

Mitrovic, Sandra, & De Weerdt, jochen. (2019). Churn Prediction using representation learning with guided random walks. Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks, SocInf MAISoN, Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks, SocInf MAISoN; 2019.

Pasi, G, Piwowarski, B, Azzopardi, L, & Hanbury, A. (2018). Benefits of Using Symmetric Loss in Recommender Systems. ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018), 10772, 345-356.

De Winter, Sam, Decuypere, Tim, Mitrovic, Sandra, Baesens, Bart, & De Weerdt, Jochen. (2018). Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction. 2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 1234-1241.

Mitrovic, Sandra, Singh, Gaurav, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2017). Scalable rfm-enriched representation learning for churn prediction. Proceedings of the Fourth IEEE International Conference on Data Science and Advanced Analytics (DSAA2017), 2018-January, 79-88.

Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2017). Churn prediction using dynamic rfm-augmented node2vec. Proceedings of the Third International Workshop on Dynamics in and of Networks, ECML-PKDD 2017, 10708 LNCS, 122-138.

Mothe, J, Savoy, J, Kamps, J, PinelSauvagnat, K, Jones, GJF, SanJuan, E, . . . Ferro, N. (2015). Summarizing Citation Contexts of Scientific Publications. EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION, 9283, 154-165.

Abstracts, Presentations, Posters

Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2018). Churn Prediction in Telco using Adapted node2vec on RFM-enriched CDR graphs. TBA.

Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2017). On added value of feature engineering for churn prediction.

Van Calster, Tine, Reusens, Michael, Oskarsdottir, María, Mitrovic, Sandra, Lismont, Jasmien, De Weerdt, Jochen, . . . Vanthienen, Jan. (2017). What's in the network? A stepwise overview of working with networked data in R.