22 research outputs found
Social Learning over Weakly-Connected Graphs
In this paper, we study diffusion social learning over weakly-connected
graphs. We show that the asymmetric flow of information hinders the learning
abilities of certain agents regardless of their local observations. Under some
circumstances that we clarify in this work, a scenario of total influence (or
"mind-control") arises where a set of influential agents ends up shaping the
beliefs of non-influential agents. We derive useful closed-form expressions
that characterize this influence, and which can be used to motivate design
problems to control it. We provide simulation examples to illustrate the
results.Comment: To appear in 2017 in the IEEE Transactions on Signal and Information
Processing over Network
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of
large-scale structured data, especially those related to complex domains such
as networks and graphs, are one of the key questions in modern machine
learning. Graph signal processing (GSP), a vibrant branch of signal processing
models and algorithms that aims at handling data supported on graphs, opens new
paths of research to address this challenge. In this article, we review a few
important contributions made by GSP concepts and tools, such as graph filters
and transforms, to the development of novel machine learning algorithms. In
particular, our discussion focuses on the following three aspects: exploiting
data structure and relational priors, improving data and computational
efficiency, and enhancing model interpretability. Furthermore, we provide new
perspectives on future development of GSP techniques that may serve as a bridge
between applied mathematics and signal processing on one side, and machine
learning and network science on the other. Cross-fertilization across these
different disciplines may help unlock the numerous challenges of complex data
analysis in the modern age