Joint network topology inference via a shared graphon model
We consider the problem of estimating the topology of multiple networks from nodal
observations, where these networks are assumed to be drawn from the same (unknown) …
observations, where these networks are assumed to be drawn from the same (unknown) …
An outlier-robust smoothness-based graph learning approach
H Araghi, M Babaie-Zadeh - Signal Processing, 2023 - Elsevier
Graph learning (GL) is a tool for finding direct relationships between the nodes of a network,
and hence, inferring the graph topology from the data. Recently, many GL algorithms have …
and hence, inferring the graph topology from the data. Recently, many GL algorithms have …
GRACGE: Graph signal clustering and multiple graph estimation
In graph signal processing (GSP), complex datasets arise from several underlying graphs
and in the presence of heterogeneity. Graph learning from heterogeneous graph signals …
and in the presence of heterogeneity. Graph learning from heterogeneous graph signals …
Demixing and topology identification for mixed graph signals
J Hong, X Dai - 2021 5th International Conference on …, 2021 - ieeexplore.ieee.org
Graph learning (GL) plays a pivotal role in graph signal processing (GSP) for inferring the
underlying signal structure. However, most of the recent works only focus on the single …
underlying signal structure. However, most of the recent works only focus on the single …