3 papers
cs.SI2024
Simplifying complex machine learning by linearly separable network embedding spaces
Alexandros Xenos, Noel-Malod Dognin, Natasa Przulj
Low-dimensional embeddings are a cornerstone in the modelling and analysis of complex networks. However, most existing approaches for mining network embedding spaces rely on comput…
cs.SI2024
Graphlets correct for the topological information missed by random walks
Sam F. L. Windels, Noel Malod-Dognin, Natasa Przulj
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding…
q-bio.OT2024
Simplicity within biological complexity
Natasa Przulj, Noel Malod-Dognin
Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients i…