1,187 research outputs found
Topology Design for Optimal Network Coherence
We consider a network topology design problem in which an initial undirected
graph underlying the network is given and the objective is to select a set of
edges to add to the graph to optimize the coherence of the resulting network.
We show that network coherence is a submodular function of the network
topology. As a consequence, a simple greedy algorithm is guaranteed to produce
near optimal edge set selections. We also show that fast rank one updates of
the Laplacian pseudoinverse using generalizations of the Sherman-Morrison
formula and an accelerated variant of the greedy algorithm can speed up the
algorithm by several orders of magnitude in practice. These allow our
algorithms to scale to network sizes far beyond those that can be handled by
convex relaxation heuristics
On the Smallest Eigenvalue of Grounded Laplacian Matrices
We provide upper and lower bounds on the smallest eigenvalue of grounded
Laplacian matrices (which are matrices obtained by removing certain rows and
columns of the Laplacian matrix of a given graph). The gap between the upper
and lower bounds depends on the ratio of the smallest and largest components of
the eigenvector corresponding to the smallest eigenvalue of the grounded
Laplacian. We provide a graph-theoretic bound on this ratio, and subsequently
obtain a tight characterization of the smallest eigenvalue for certain classes
of graphs. Specifically, for Erdos-Renyi random graphs, we show that when a
(sufficiently small) set of rows and columns is removed from the Laplacian,
and the probability of adding an edge is sufficiently large, the smallest
eigenvalue of the grounded Laplacian asymptotically almost surely approaches
. We also show that for random -regular graphs with a single row and
column removed, the smallest eigenvalue is . Our bounds
have applications to the study of the convergence rate in continuous-time and
discrete-time consensus dynamics with stubborn or leader nodes
A network model of mass media opinion dynamics
The coexistence of diverse opinions is necessary for a pluralistic society in which people can confront ideas and make informed choices. The media functions as a primary source of information, and diversity across news sources in the media forms the basis for wider discourse in the public. However, due to numerous economic and social pressures, news sources frequently co-orient their content through what is known as intermedia agenda-setting. Past research on the subject has examined relationships between individual news sources. However, to understand emergent behaviour such as opinion diversity, we cannot simply analyse individual relationships in isolation, but instead need to view the media as a complex system of many interacting entities. The aim of this thesis is to develop and empirically test a method for understanding the network effects that intermedia agenda-setting has on the diversity of expressed opinions within the media. Utilising latent signals extracted from news articles, we put forward a methodology for inferring networks that capture how agendas propagate between news sources via the opinions they express on various topics. By applying this approach to a large dataset of news articles published by globally and locally prominent news organisations, we identify how the structure of intermedia networks is indicative of the level of opinion diversity across various topics. We then develop a theoretical model of opinion dynamics in noisy domains that is motivated by the empirical observations of intermedia agenda formation. From this, we derive a general analytical expression for opinion diversity that holds for any network and depends on the network's topology through its spectral properties alone. Finally, we validate the analytical expression in a linear model against empirical data. This thesis aids our understanding of how to model emergent behaviour of the media and promote diversity
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