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Graph Implementations for Nonsmooth Convex Programs
Convex optimization nonsmooth optimization optimization modeling languages semidefinite programming
2015/7/9
We describe graph implementations, a generic method for representing a convex function via its epigraph, described in a disciplined convex programming framework. This simple and natural idea allows a ...
Behavior of Graph Laplacians on Manifolds with Boundary
Graph Laplacians Behavior Manifolds Boundary
2011/6/21
In manifold learning, algorithms based on graph Laplacians constructed from data have received
considerable attention both in practical applications and theoretical analysis. In particular, the
conv...
Deconvolution of mixing time series on a graph
Deconvolution of mixing time latent time series state-space model
2011/6/17
In many applications we are interested in making
inference on latent time series from indirect
measurements, which are often low-dimensional
projections resulting from mixing or aggregation.
Posit...
Modeling Network Evolution Using Graph Motifs
Modeling Network Graph Motifs simulating network evolution
2011/6/16
Network structures are extremely important to the study of political science. Much of the data
in its sub elds are naturally represented as networks. This includes trade, diplomatic and con
ict
rel...
Limit theorems for an epidemic model on the complete graph
Epidemic model random walk complete graph Markov chains Law of Large Numbers Central Limit Theorem
2009/6/12
We study the following random walks system on the complete graph with n vertices.At time zero,there is a number of active and inactive particles living on the vertices.Active particles move as continu...
Component sizes of the random graph outside the scaling window
random graphs percolation martingales
2009/6/12
Component sizes of the random graph outside the scaling window.
Degree distribution nearby the origin of a preferential attachment graph
preferential attachment graph Degree distribution
2009/3/30
In a 2-parameter scale free model of random graphs it is shown that the asymptotic degree distribution is the same in the neighbourhood of every vertex. This degree distribution is still a power law w...
On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models
Geometry Discrete Exponential Families Exponential Random Graph Models
2010/3/17
There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especially in connection with...