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An Introduction to Bayesian Network Theory and Usage

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Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Pierre Vandergheynst, Xavier Bresson, Rodrigo Cerqueira Gonzalez Pena

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot ...

Ieee2016

Infectious Disease Containment Based on a Wireless Sensor System

Marcel Salathé, Xiao Sun

Infectious diseases pose a serious threat to public health due to its high infectivity and potentially high mortality. One of the most effective ways to protect people from being infected by these diseases is through vaccination. However, due to various re ...

Ieee-Inst Electrical Electronics Engineers Inc2016

How to learn a graph from smooth signals

Vasilis Kalofolias

We propose a framework that learns the graph structure underlying a set of smooth signals. Given a real m by n matrix X whose rows reside on the vertices of an unknown graph, we learn the edge weights w under the smoothness assumption that trace(X^TLX) is ...

2016

A Multiscale Pyramid Transform for Graph Signals

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weighted graph, as they do not capture the intrinsic topology of the graph data ...

Institute of Electrical and Electronics Engineers2016

Information Exchange and Learning Dynamics Over Weakly Connected Adaptive Networks

Ali H. Sayed, Bicheng Ying

This paper examines the learning mechanism of adaptive agents over weakly connected graphs and reveals an interesting behavior on how information flows through such topologies. The results clarify how asymmetries in the exchange of data can mask local info ...

IEEE2016

Diffusion social learning over weakly-connected graphs

Ali H. Sayed, Bicheng Ying

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 clarif ...

IEEE2016

Decomposition of Multiple Packings with Subquadratic Union Complexity

János Pach, Bartosz Maria Walczak

Suppose k is a positive integer and X is a k-fold packing of the plane by infinitely many arc-connected compact sets, which means that every point of the plane belongs to at most k sets. Suppose there is a function f(n) = o(n(2)) with the property that any ...

Cambridge Univ Press2016

A Precise Threshold For Quasi-Ramsey Numbers

János Pach

We consider the variation of Ramsey numbers introduced by Erdos and Pach [J. Graph Theory, 7 (1983), pp. 137-147], where instead of seeking complete or independent sets we only seek a t-homogeneous set, a vertex subset that induces a subgraph of minimum de ...

Siam Publications2015

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Pierre Vandergheynst, David Shuman

We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted graphs. While the filters used in previous spectral graph wavelet constructions ...

Institute of Electrical and Electronics Engineers2015

Heuristic-driven Graph Wavelet Modeling of Complex Terrain

Alexandre Buttler, François Golay, Alexander Ludwig Johannes Peringer, Ioana Stoicescu

We present a novel method for building a multiresolution representation of large digital surface models. The surface points coincide with the nodes of a planar graph which can be processed using a critically sampled, invertible lifting scheme. To drive the ...

Spie-Int Soc Optical Engineering2015