Efficient learning of balanced signature graphs
The novel concept of signature graphs extends signed graphs by admitting multiple types of
partial similarity/agreement or dissimilarity/disagreement. Extending the concept of …
partial similarity/agreement or dissimilarity/disagreement. Extending the concept of …
Fair community detection and structure learning in heterogeneous graphical models
Inference of community structure in probabilistic graphical models may not be consistent
with fairness constraints when nodes have demographic attributes. Certain demographics …
with fairness constraints when nodes have demographic attributes. Certain demographics …
Clusterpath Gaussian Graphical Modeling
DJW Touw, A Alfons, PJF Groenen, I Wilms - arXiv preprint arXiv …, 2024 - arxiv.org
Graphical models serve as effective tools for visualizing conditional dependencies between
variables. However, as the number of variables grows, interpretation becomes increasingly …
variables. However, as the number of variables grows, interpretation becomes increasingly …
Multilayer Graph Clustering with Optimized Node Embedding
M El Gheche, P Frossard - 2021 IEEE Data Science and …, 2021 - ieeexplore.ieee.org
We are interested in multilayer graph clustering, which aims at dividing the graph nodes into
categories or communities. To do so, we propose to learn a clustering-friendly embedding of …
categories or communities. To do so, we propose to learn a clustering-friendly embedding of …