Visually Controllable Data Mining Methods. - Kai Puolamäki; Panagiotis Papapetrou; Jefrey Lijffijt (ICDM Workshops, 2010)

Tracking your steps on the track: body sensor recordings of a controlled walking experiment. - Jefrey Lijffijt; Panagiotis Papapetrou; Jaakko Hollmén (PETRA, 2010)

Benchmarking dynamic time warping for music retrieval. - Jefrey Lijffijt; Panagiotis Papapetrou; Jaakko Hollmén; Vassilis Athitsos (PETRA, 2010)

Analyzing Word Frequencies in Large Text Corpora Using Inter-arrival Times and Bootstrapping. - Jefrey Lijffijt; Panagiotis Papapetrou; Kai Puolamäki; Heikki Mannila (ECML/PKDD (2), 2011)

Size Matters: Finding the Most Informative Set of Window Lengths. - Jefrey Lijffijt; Panagiotis Papapetrou; Kai Puolamäki (ECML/PKDD (2), 2012)

A Fast and Simple Method for Mining Subsequences with Surprising Event Counts. - Jefrey Lijffijt (ECML/PKDD (1), 2013)

Computational methods for comparison and exploration of event sequences. - Jefrey Lijffijt (2013)

A statistical significance testing approach to mining the most informative set of patterns. - Jefrey Lijffijt; Panagiotis Papapetrou; Kai Puolamäki (Data Min. Knowl. Discov., 2014)

Covering the Egonet: A Crowdsourcing Approach to Social Circle Discovery on Twitter. - Karmen Lata Dykstra; Jefrey Lijffijt; Aristides Gionis (ICWSM, 2015)

Interactively Exploring Supply and Demand in the UK Independent Music Scene. - Matt McVicar; Cédric Mesnage; Jefrey Lijffijt; Tijl De Bie (ECML/PKDD (3), 2015)

Supply and demand of independent UK music artists on the web. - Matt McVicar; Cédric Mesnage; Jefrey Lijffijt; Eirini Spyropoulou; Tijl De Bie (WebSci, 2015)

Size matters: choosing the most informative set of window lengths for mining patterns in event sequences. - Jefrey Lijffijt; Panagiotis Papapetrou; Kai Puolamäki (Data Min. Knowl. Discov., 2015)

Informative data projections: a framework and two examples. - Tijl De Bie; Jefrey Lijffijt; Raúl Santos-Rodríguez; Bo Kang (ESANN, 2016)

Direct Mining of Subjectively Interesting Relational Patterns. - Tias Guns; Achille Aknin; Jefrey Lijffijt; Tijl De Bie (ICDM, 2016)

Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations. - Bo Kang; Jefrey Lijffijt; Raúl Santos-Rodriguez; Tijl De Bie (KDD, 2016)

Detecting trends in twitter time series. - Tijl De Bie; Jefrey Lijffijt; Cédric Mesnage; Raúl Santos-Rodriguez (MLSP, 2016)

A Tool for Subjective and Interactive Visual Data Exploration. - Bo Kang; Kai Puolamäki; Jefrey Lijffijt; Tijl De Bie (ECML/PKDD (3), 2016)

Interactive Visual Data Exploration with Subjective Feedback. - Kai Puolamäki; Bo Kang; Jefrey Lijffijt; Tijl De Bie (ECML/PKDD (2), 2016)

P-N-RMiner: a generic framework for mining interesting structured relational patterns. - Jefrey Lijffijt; Eirini Spyropoulou; Bo Kang; Tijl De Bie (Int. J. Data Sci. Anal., 2016)

Significance testing of word frequencies in corpora. - Jefrey Lijffijt; Terttu Nevalainen; Tanja Säily; Panagiotis Papapetrou; Kai Puolamäki; Heikki Mannila (Digit. Scholarsh. Humanit., 2016)

SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees. - Matt McVicar; Benjamin Sach; Cédric Mesnage; Jefrey Lijffijt; Eirini Spyropoulou; Tijl De Bie (Pattern Recognit. Lett., 2016)

Hierarchical Novelty Detection. - Paolo Simeone; Raúl Santos-Rodríguez; Matt McVicar; Jefrey Lijffijt; Tijl De Bie (IDA, 2017)

Subjectively Interesting Connecting Trees. - Florian Adriaens; Jefrey Lijffijt; Tijl De Bie (ECML/PKDD (2), 2017)

Subjectively Interesting Subgroup Discovery on Real-Valued Targets. - Jefrey Lijffijt; Bo Kang; Wouter Duivesteijn; Kai Puolamäki; Emilia Oikarinen; Tijl De Bie (ICDE, 2018)

Quantifying and Minimizing Risk of Conflict in Social Networks. - Xi Chen; Jefrey Lijffijt; Tijl De Bie (KDD, 2018)

From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths. - Florian Adriaens; Tijl De Bie; Aristides Gionis; Jefrey Lijffijt; Polina Rozenshtein (CoRR, 2018)

SICA: subjectively interesting component analysis. - Bo Kang; Jefrey Lijffijt; Raúl Santos-Rodríguez; Tijl De Bie (Data Min. Knowl. Discov., 2018)

Discovering Interesting Cycles in Directed Graphs. - Florian Adriaens; Çigdem Aslay; Tijl De Bie; Aristides Gionis; Jefrey Lijffijt (CIKM, 2019)

Conditional Network Embeddings. - Bo Kang; Jefrey Lijffijt; Tijl De Bie (ICLR (Poster), 2019)

Contrastive Antichains in Hierarchies. - Anes Bendimerad; Jefrey Lijffijt; Marc Plantevit; Céline Robardet; Tijl De Bie (KDD, 2019)

EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. - Alexandru Mara; Jefrey Lijffijt; Tijl De Bie (EDML@SDM, 2019)

Opinion Dynamics with Backfire Effect and Biased Assimilation. - Xi Chen; Panayiotis Tsaparas; Jefrey Lijffijt; Tijl De Bie (CoRR, 2019)

ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions. - Bo Kang; Jefrey Lijffijt; Tijl De Bie (CoRR, 2019)

Mining Subjectively Interesting Attributed Subgraphs. - Anes Bendimerad; Ahmad Mel; Jefrey Lijffijt; Marc Plantevit; Céline Robardet; Tijl De Bie (CoRR, 2019)

Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information. - Bo Kang; Dario García-García; Jefrey Lijffijt; Raúl Santos-Rodríguez; Tijl De Bie (CoRR, 2019)

Subjectively interesting connecting trees and forests. - Florian Adriaens; Jefrey Lijffijt; Tijl De Bie (Data Min. Knowl. Discov., 2019)

SIMIT: Subjectively Interesting Motifs in Time Series. - Junning Deng; Jefrey Lijffijt; Bo Kang; Tijl De Bie (Entropy, 2019)

CSNE: Conditional Signed Network Embedding. - Alexandru Mara; Yoosof Mashayekhi; Jefrey Lijffijt; Tijl De Bie (CIKM, 2020)

Block-Approximated Exponential Random Graphs. - Florian Adriaens; Alexandru Mara; Jefrey Lijffijt; Tijl De Bie (DSAA, 2020)

Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress? - Alexandru Cristian Mara; Jefrey Lijffijt; Tijl De Bie (DSAA, 2020)

FONDUE: Framework for Node Disambiguation Using Network Embeddings. - Ahmad Mel; Bo Kang; Jefrey Lijffijt; Tijl De Bie (DSAA, 2020)

Gibbs Sampling Subjectively Interesting Tiles. - Anes Bendimerad; Jefrey Lijffijt; Marc Plantevit; Céline Robardet; Tijl De Bie (IDA, 2020)

Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. - Junning Deng; Bo Kang; Jefrey Lijffijt; Tijl De Bie (SDM, 2020)

ALPINE: Active Link Prediction using Network Embedding. - Xi Chen; Bo Kang; Jefrey Lijffijt; Tijl De Bie (CoRR, 2020)

Scalable Dyadic Independence Models with Local and Global Constraints. - Florian Adriaens; Alexandru Mara; Jefrey Lijffijt; Tijl De Bie (CoRR, 2020)

Network Representation Learning for Link Prediction: Are we improving upon simple heuristics? - Alexandru Mara; Jefrey Lijffijt; Tijl De Bie (CoRR, 2020)

Relaxing the strong triadic closure problem for edge strength inference. - Florian Adriaens; Tijl De Bie; Aristides Gionis; Jefrey Lijffijt; Antonis Matakos; Polina Rozenshtein (Data Min. Knowl. Discov., 2020)

SIAS-miner: mining subjectively interesting attributed subgraphs. - Anes Bendimerad; Ahmad Mel; Jefrey Lijffijt; Marc Plantevit; Céline Robardet; Tijl De Bie (Data Min. Knowl. Discov., 2020)

Interactive visual data exploration with subjective feedback: an information-theoretic approach. - Kai Puolamäki; Emilia Oikarinen; Bo Kang; Jefrey Lijffijt; Tijl De Bie (Data Min. Knowl. Discov., 2020)

A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback. - Bo Kang; Kai Puolamäki; Jefrey Lijffijt; Tijl De Bie (IEEE Trans. Knowl. Data Eng., 2020)

Quantifying and Reducing Imbalance in Networks. - Yoosof Mashayekhi; Bo Kang; Jefrey Lijffijt; Tijl De Bie (HR@RecSys, 2021)

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction. - Xi Chen; Bo Kang; Jefrey Lijffijt; Tijl De Bie (PKDD/ECML Workshops (2), 2021)

Explanations for Network Embedding-Based Link Predictions. - Bo Kang; Jefrey Lijffijt; Tijl De Bie (PKDD/ECML Workshops (1), 2021)

ExClus: Explainable Clustering on Low-dimensional Data Representations. - Xander Vankwikelberge; Bo Kang; Edith Heiter; Jefrey Lijffijt (CoRR, 2021)

Mining explainable local and global subgraph patterns with surprising densities. - Junning Deng; Bo Kang; Jefrey Lijffijt; Tijl De Bie (Data Min. Knowl. Discov., 2021)

Conditional t-SNE: more informative t-SNE embeddings. - Bo Kang; Dario García-García; Jefrey Lijffijt; Raúl Santos-Rodríguez; Tijl De Bie (Mach. Learn., 2021)

Topologically Regularized Data Embeddings. - Robin Vandaele; Bo Kang; Jefrey Lijffijt; Tijl De Bie; Yvan Saeys (ICLR, 2022)

A Systematic Evaluation of Node Embedding Robustness. - Alexandru Cristian Mara; Jefrey Lijffijt; Stephan Günnemann; Tijl De Bie (LoG, 2022)

Evaluating Representation Learning and Graph Layout Methods for Visualization. - Edith Heiter; Bo Kang; Tijl De Bie; Jefrey Lijffijt (IEEE Computer Graphics and Applications, 2022)

Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges. - Jefrey Lijffijt; Dimitra Gkorou; Pieter Van Hertum; Alexander Ypma; Mykola Pechenizkiy; Joaquin Vanschoren (SIGKDD Explor., 2022)

EvalNE: A framework for network embedding evaluation. - Alexandru Mara; Jefrey Lijffijt; Tijl De Bie (SoftwareX, 2022)

Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors. - Edith Heiter; Bo Kang; Ruth Seurinck; Jefrey Lijffijt (IDA, 2023)

ReCon: Reducing Congestion in Job Recommendation using Optimal Transport. - Yoosof Mashayekhi; Bo Kang; Jefrey Lijffijt; Tijl De Bie (RecSys, 2023)

Gaussian Embedding of Temporal Networks. - Raphaël Romero; Jefrey Lijffijt; Riccardo Rastelli; Marco Corneli; Tijl De Bie (IEEE Access, 2023)

Topologically Regularized Data Embeddings. - Edith Heiter; Robin Vandaele; Tijl De Bie; Yvan Saeys; Jefrey Lijffijt (CoRR, 2023)

New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs. - Raphaël Romero; Tijl De Bie; Jefrey Lijffijt (CoRR, 2023)

Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE. - Edith Heiter; Liesbet Martens; Ruth Seurinck; Martin Guilliams; Tijl De Bie; Yvan Saeys; Jefrey Lijffijt (ECML/PKDD (8), 2024)

Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization. - Edith Heiter; Robin Vandaele; Tijl De Bie; Yvan Saeys; Jefrey Lijffijt (IEEE Access, 2024)

Scalable Job Recommendation With Lower Congestion Using Optimal Transport. - Yoosof Mashayekhi; Bo Kang; Jefrey Lijffijt; Tijl De Bie (IEEE Access, 2024)

An Empirical Evaluation of Network Representation Learning Methods. - Alexandru Cristian Mara; Jefrey Lijffijt; Tijl De Bie (Big Data, 2024)

Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms. - Raphaël Romero; Maarten Buyl; Tijl De Bie; Jefrey Lijffijt (CoRR, 2024)

Large Language Models Reflect the Ideology of their Creators. - Maarten Buyl; Alexander Rogiers; Sander Noels; Iris Dominguez-Catena; Edith Heiter; Raphaël Romero; Iman Johary; Alexandru Cristian Mara; Jefrey Lijffijt; Tijl De Bie (CoRR, 2024)

GREASE: Graph Imbalance Reduction by Adding Sets of Edges. - Yoosof Mashayekhi; Bo Kang; Jefrey Lijffijt; Tijl De Bie (IEEE Trans. Knowl. Data Eng., 2024)

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources. - Nan Li; Bo Kang; Jefrey Lijffijt; Tijl De Bie (ACM Trans. Intell. Syst. Technol., 2024)

A Challenge-based Survey of E-recruitment Recommendation Systems. - Yoosof Mashayekhi; Nan Li; Bo Kang; Jefrey Lijffijt; Tijl De Bie (ACM Comput. Surv., 2024)

What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices. - Sander Noels; Guillaume Bied; Maarten Buyl; Alexander Rogiers; Yousra Fettach; Jefrey Lijffijt; Tijl De Bie (CoRR, 2025)

InfoClus: Informative Clustering of High-dimensional Data Embeddings. - Fuyin Lai; Edith Heiter; Guillaume Bied; Jefrey Lijffijt (CoRR, 2025)

BiMi Sheets: Infosheets for bias mitigation methods. - MaryBeth Defrance; Guillaume Bied; Maarten Buyl; Jefrey Lijffijt; Tijl De Bie (CoRR, 2025)

SimHawNet: a modified Hawkes process for temporal network simulation. - Mathilde Perez; Raphaël Romero; Bo Kang; Tijl De Bie; Jefrey Lijffijt; Charlotte Laclau (Data Min. Knowl. Discov., 2025)