Alexandru Mara, Jefrey Lijffijt, Tijl De Bie. EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. 1st EDML/SDM Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, 2019.
Robin Vandaele, Tijl De Bie, and Yvan Saeys. Local topological data analysis to uncover the global structure of data approaching graph-structured topologies. In Proceedings of the European Conference on Machine Learning[ . . . ]
Junning Deng, Jefrey Lijffijt, Bo Kang, Tijl De Bie. Subjectively Interesting Motifs in Time Series. 3rd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data.
Poster accepted at IEEE VAST 2018 Abstract: Projecting data down to two dimensions to visualize in a scatterplot is one of the basic building blocks of visualization. While there are[ . . . ]
Bo Kang, Jefrey Lijffijt, and Tijl De Bie. Conditional Network Embeddings. Under review. Manuscript posted at https://arxiv.org/abs/1805.07544
Xi Chen, Jefrey Lijffijt, and Tijl De Bie. Quantifying and Minimizing Risk of Conflict in Social Networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data[ . . . ]
Anes Bendimerad*, Ahmad Mel*, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie. “Mining Subjectively Interesting Attributed Subgraphs.” Under review, 2018. (*equal contributors)
Bo Kang, Jefrey Lijffijt, Raul Santos Rodriguez, Tijl De Bie. “SICA: Subjectively Interesting Component Analysis.” Volume 32, Issue 4, pp 949–987, July 2018. (springer)
A short version of our paper “Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach” is to appear at the IEEE International Conference on Data Engineering (ICDE) 2018. A[ . . . ]
Adriaens, Florian, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, and Polina Rozenshtein. “From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths.” arXiv preprint arXiv:1802.03549 (2018).