Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Networks have provided a representation for a wide range of real systems, including
communication networks, money transfer networks and biological systems. Communities …

Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with
millions of people adversely affected. This is coupled with a trend in which the intensity and …

Graph structured datasets arise naturally in many fields including biology with protein-to-
protein interaction networks, ecology with predator-prey networks and economy with …

L'analyse des réseaux sociaux est fondée sur l'étude des interactions sociales pour la
compréhension des comportements individuels et collectifs au sein des systèmes …

Graph clustering problems typically aim to partition the graph nodes such that two nodes
belong to the same partition set if and only if they are similar. Correlation Clustering is a …

Networks have long been used to describe systems of interacting elements. Nevertheless, it
is only recently that rigorous mathematical analysis of networked models has found a vital …

Graph-structured datasets arise naturally in many fields including biology with protein-to-
protein interaction networks, ecology with predator-prey networks, and economy with …

Graph clustering is widely-studied unsupervised learning problem in which the task is to
group similar entities together based on observed pairwise entity interactions. This problem …