
Learning Graphs from Smooth and GraphStationary Signals with Hidden Variables
Networktopology inference from (vertex) signal observations is a promin...
read it

Joint Inference of Multiple Graphs from Matrix Polynomials
Inferring graph structure from observations on the nodes is an important...
read it

SemiBlind Inference of Topologies and Dynamical Processes over Graphs
Network science provides valuable insights across numerous disciplines i...
read it

Learning with hidden variables
Learning and inferring features that generate sensory input is a task co...
read it

Active Learning for Node Classification in Assortative and Disassortative Networks
In many realworld networks, nodes have class labels, attributes, or var...
read it

Learning the Dimensionality of Hidden Variables
A serious problem in learning probabilistic models is the presence of hi...
read it

Causal Discovery in HighDimensional Point Process Networks with Hidden Nodes
Thanks to technological advances leading to nearcontinuous time observa...
read it
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference. However, current approaches are limited by typically focusing on inferring single networks, and they assume that observations from all nodes are available. First, many contemporary setups involve multiple related networks, and second, it is often the case that only a subset of nodes is observed while the rest remain hidden. Motivated by these facts, we introduce a joint graph topology inference method that models the influence of the hidden variables. Under the assumptions that the observed signals are stationary on the sought graphs and the graphs are closely related, the joint estimation of multiple networks allows us to exploit such relationships to improve the quality of the learned graphs. Moreover, we confront the challenging problem of modeling the influence of the hidden nodes to minimize their detrimental effect. To obtain an amenable approach, we take advantage of the particular structure of the setup at hand and leverage the similarity between the different graphs, which affects both the observed and the hidden nodes. To test the proposed method, numerical simulations over synthetic and realworld graphs are provided.
READ FULL TEXT
Comments
There are no comments yet.