25 citations · 147 across the 36 of their papers we have counts for
38 papers
How smoothing the affinity matrix affects neighborhood preservation in t-SNE
Shirin Mohebi, Guillaume Bied, Jefrey Lijffijt
Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local neighborh…
How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
Mathilde Perez, Raphaël Romero, Jefrey Lijffijt +1
Link prediction models are increasingly used to recommend interactions in evolving networks, yet their impact on network structure is typically assessed from static snapshots. In p…
Are LLMs ready to help non-expert users to make charts of official statistics data?
Gadir Suleymanli, Alexander Rogiers, Lucas Lageweg +1
In this time when biased information, deep fakes, and propaganda proliferate, the accessibility of reliable data sources is more important than ever. National statistical institute…
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
InfoClus: Informative Clustering of High-dimensional Data Embeddings
Fuyin Lai, Edith Heiter, Guillaume Bied +1
Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often d…
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices
Sander Noels, Guillaume Bied, Maarten Buyl +4
Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to…
Large Language Models Reflect the Ideology of their Creators
Maarten Buyl, Alexander Rogiers, Sander Noels +8
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These…
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE
Edith Heiter, Liesbet Martens, Ruth Seurinck +4
This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize p…
Gaussian Embedding of Temporal Networks
Raphaël Romero, Jefrey Lijffijt, Riccardo Rastelli +2
Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuo…
Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
Raphaël Romero, Maarten Buyl, Tijl De Bie +1
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that…
New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs
Raphaël Romero, Tijl De Bie, Jefrey Lijffijt
There is a fast-growing body of research on predicting future links in dynamic networks, with many new algorithms. Some benchmark data exists, and performance evaluations commonly…
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
Nan Li, Bo Kang, Jefrey Lijffijt +1
In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness. We i…
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt +1
Recommender systems may suffer from congestion, meaning that there is an unequal distribution of the items in how often they are recommended. Some items may be recommended much mor…
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
Edith Heiter, Bo Kang, Ruth Seurinck +1
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond…
Topologically Regularized Data Embeddings
Edith Heiter, Robin Vandaele, Tijl De Bie +2
Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topolo…
A Systematic Evaluation of Node Embedding Robustness
Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann +1
Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks. The popularity of these methods has g…
A challenge-based survey of e-recruitment recommendation systems
Yoosof Mashayekhi, Nan Li, Bo Kang +2
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for…
SimHawNet: A Modified Hawkes Process for Temporal Network Simulation
Mathilde Perez, Raphaël Romero, Bo Kang +3
Temporal networks allow representing connections between objects while incorporating the temporal dimension. While static network models can capture unchanging topological regulari…
ExClus: Explainable Clustering on Low-dimensional Data Representations
Xander Vankwikelberge, Bo Kang, Edith Heiter +1
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret. We consider how to support u…
Topologically Regularized Data Embeddings
Robin Vandaele, Bo Kang, Jefrey Lijffijt +2
Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available…
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
Xi Chen, Bo Kang, Jefrey Lijffijt +1
In today's networked society, many real-world problems can be formalized as predicting links in networks, such as Facebook friendship suggestions, e-commerce recommendations, and t…
CSNE: Conditional Signed Network Embedding
Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt +1
Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the…
FONDUE: A Framework for Node Disambiguation Using Network Embeddings
Ahmad Mel, Bo Kang, Jefrey Lijffijt +1
Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest fo…
ALPINE: Active Link Prediction using Network Embedding
Xi Chen, Bo Kang, Jefrey Lijffijt +1
Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations,…