most citedGraph Signal Processing -- Part II: Processing and Analyzing Signals on Graphs

11 citations · 19 across the 4 of their papers we have counts for

collaborators

8 papers

cs.IT20206 cited

Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +4

Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition,…

eess.SP2019

Graph Theory and Metro Traffic Modelling

Bruno Scalzo Dees, Anthony G. Constantinides, Danilo P. Mandic

In this article we demonstrate how graph theory can be used to identify those stations in the London underground network which have the greatest influence on the functionality of t…

eess.SP2019

Portfolio Cuts: A Graph-Theoretic Framework to Diversification

Bruno Scalzo Dees, Ljubisa Stankovic, Anthony G. Constantinides +1

Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate…

cs.IT201911 cited

Graph Signal Processing -- Part II: Processing and Analyzing Signals on Graphs

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +3

The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to ad…

eess.SP2019

Unitary Shift Operators on a Graph

Bruno Scalzo Dees, Ljubisa Stankovic, Milos Dakovic +2

A unitary shift operator (GSO) for signals on a graph is introduced, which exhibits the desired property of energy preservation over both backward and forward graph shifts. For rig…

eess.SP2019

A Class of Doubly Stochastic Shift Operators for Random Graph Signals and their Boundedness

Bruno Scalzo Dees, Ljubisa Stankovic, Milos Dakovic +2

A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper -boundedness for locally stationary random graph signals…