This paper aims to provide a comprehensive critical overview on how entities and their
interactions in Complex Networked Systems (CNS) are modelled across disciplines as they …

In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting
the underlying topological relationships between them. The difficulty lies in how to design a …

We tackle the network topology inference problem by utilizing Laplacian constrained
Gaussian graphical models, which recast the task as estimating a precision matrix in the …

Graph topology inference is a significant task in many application domains. Existing
approaches are mostly limited to learning a single graph assuming that the observed data is …

Modern quantitative finance and portfolio-based investment hinge on the dependence
structure among financial instruments (like stocks) for return prediction, risk management …

In graph signal processing (GSP), complex datasets arise from several underlying graphs
and in the presence of heterogeneity. Graph learning from heterogeneous graph signals …

We consider the problem of inferring the underlying graph topology from smooth graph
signals in a novel but practical scenario where data are located in distributed clients and are …

Identifying the topology underlying a set of time series is useful for tasks such as prediction,
denoising, and data completion. Vector autoregressive (VAR) model-based topologies …