218 research outputs found
On Stability and Consensus of Signed Networks: A Self-loop Compensation Perspective
Positive semidefinite is not an inherent property of signed Laplacians, which
renders the stability and consensus of multi-agent system on undirected signed
networks intricate. Inspired by the correlation between diagonal dominance and
spectrum of signed Laplacians, this paper proposes a self-loop compensation
mechanism in the design of interaction protocol amongst agents and examines the
stability/consensus of the compensated signed networks. It turns out that
self-loop compensation acts as exerting a virtual leader on these agents that
are incident to negative edges, steering whom towards origin. Analytical
connections between self-loop compensation and the collective behavior of the
compensated signed network are established. Necessary and/or sufficient
conditions for predictable cluster consensus of signed networks via self-loop
compensation are provided. The optimality of self-loop compensation is
discussed. Furthermore, we extend our results to directed signed networks where
the symmetry of signed Laplacian is not free. Simulation examples are provided
to demonstrate the theoretical results
Isotropic covariance functions on graphs and their edges
We develop parametric classes of covariance functions on linear networks and
their extension to graphs with Euclidean edges, i.e., graphs with edges viewed
as line segments or more general sets with a coordinate system allowing us to
consider points on the graph which are vertices or points on an edge. Our
covariance functions are defined on the vertices and edge points of these
graphs and are isotropic in the sense that they depend only on the geodesic
distance or on a new metric called the resistance metric (which extends the
classical resistance metric developed in electrical network theory on the
vertices of a graph to the continuum of edge points). We discuss the advantages
of using the resistance metric in comparison with the geodesic metric as well
as the restrictions these metrics impose on the investigated covariance
functions. In particular, many of the commonly used isotropic covariance
functions in the spatial statistics literature (the power exponential,
Mat{\'e}rn, generalized Cauchy, and Dagum classes) are shown to be valid with
respect to the resistance metric for any graph with Euclidean edges, whilst
they are only valid with respect to the geodesic metric in more special cases.Comment: 6 figures, 1 tabl
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