12 citations · 29 across the 12 of their papers we have counts for
9 papers · 1 filter
Multiresolution Analysis and Statistical Thresholding on Dynamic Networks
Raphaël Romero, Tijl De Bie, Nick Heard +1
Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within eac…
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-,…
ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods
MaryBeth Defrance, Maarten Buyl, Tijl De Bie
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method…
The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer
Maarten Buyl, Tijl De Bie
Learning and reasoning over graphs is increasingly done by means of probabilistic models, e.g. exponential random graph models, graph embedding models, and graph neural networks. W…
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,…