Conditional Independence (CI) graphs are a type of Probabilistic Graphical Models that are
primarily used to gain insights about feature relationships. Each edge represents the partial …

Objective: Multimodal measurements of the same phenomena provide complementary
information and highlight different perspectives, albeit each with their own limitations. A …

Graph knowledge discovery from graph-structured data is a fascinating data mining topic in
various domains, especially in the Internet of Things, where inferring the graph structure …

In this paper, we study the problem of inferring spatially-varying Gaussian Markov random
fields (SV-GMRF) where the goal is to learn a network of sparse, context-specific GMRFs …

There is a considerable body of work in the field of computer science on the topic of sparse
graph recovery, particularly with regards to the innovative deep learning approaches that …

Learning multiple related graphs from many distributed and privacy-required resources is an
important and common task in neuroscience applications. Medical researchers can …

We consider the problem of including additional knowledge in estimating sparse Gaussian
graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and …

Recently, the Internet of Things (IoT) receives significant interest due to its rapid
development. But IoT applications still face two challenges: heterogeneity and large scale of …

JointNets: an End-to-end R package for sparse Gaussian graphical model Page 1 JointNets: an
End-to-end R package for sparse Gaussian graphical model Zhaoyang Wang, Beilun Wang …