9,059 research outputs found
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Competing edge networks
We introduce a model for a pair of nonlinear evolving networks, deļ¬ned over a common set of vertices, sub ject to edgewise competition. Each network may grow new edges spontaneously or through triad closure. Both networks
inhibit the otherās growth and encourage the otherās demise. These nonlinear stochastic competition equations yield to a mean ļ¬eld analysis resulting in a nonlinear deterministic system. There may be multiple equilibria; and bifurcations of diļ¬erent types are shown to occur within a reduced parameter space. This situation models competitive peer-to-peer communication networks such as BlackBerry Messenger displacing SMS; or instant messaging
displacing emails
Nullity Invariance for Pivot and the Interlace Polynomial
We show that the effect of principal pivot transform on the nullity values of
the principal submatrices of a given (square) matrix is described by the
symmetric difference operator (for sets). We consider its consequences for
graphs, and in particular generalize the recursive relation of the interlace
polynomial and simplify its proof.Comment: small revision of Section 8 w.r.t. v2, 14 pages, 6 figure
Sketch-based Randomized Algorithms for Dynamic Graph Regression
A well-known problem in data science and machine learning is {\em linear
regression}, which is recently extended to dynamic graphs. Existing exact
algorithms for updating the solution of dynamic graph regression problem
require at least a linear time (in terms of : the size of the graph).
However, this time complexity might be intractable in practice. In the current
paper, we utilize {\em subsampled randomized Hadamard transform} and
\textsf{CountSketch} to propose the first randomized algorithms. Suppose that
we are given an matrix embedding of the graph, where .
Let be the number of samples required for a guaranteed approximation error,
which is a sublinear function of . Our first algorithm reduces time
complexity of pre-processing to .
Then after an edge insertion or an edge deletion, it updates the approximate
solution in time. Our second algorithm reduces time complexity of
pre-processing to , where is the number of nonzero elements of . Then after
an edge insertion or an edge deletion or a node insertion or a node deletion,
it updates the approximate solution in time, with
. Finally, we show
that under some assumptions, if our first algorithm
outperforms our second algorithm and if our second
algorithm outperforms our first algorithm
Regression and Singular Value Decomposition in Dynamic Graphs
Most of real-world graphs are {\em dynamic}, i.e., they change over time.
However, while problems such as regression and Singular Value Decomposition
(SVD) have been studied for {\em static} graphs, they have not been
investigated for {\em dynamic} graphs, yet. In this paper, we introduce,
motivate and study regression and SVD over dynamic graphs. First, we present
the notion of {\em update-efficient matrix embedding} that defines the
conditions sufficient for a matrix embedding to be used for the dynamic graph
regression problem (under norm). We prove that given an
update-efficient matrix embedding (e.g., adjacency matrix), after an update
operation in the graph, the optimal solution of the graph regression problem
for the revised graph can be computed in time. We also study dynamic
graph regression under least absolute deviation. Then, we characterize a class
of matrix embeddings that can be used to efficiently update SVD of a dynamic
graph. For adjacency matrix and Laplacian matrix, we study those graph update
operations for which SVD (and low rank approximation) can be updated
efficiently
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Bistability through triadic closure
We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean ļ¬eld theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure eļ¬ect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean ļ¬eld theory predicts bistable dynamics, and computational results conļ¬rm this prediction. We also discuss the calibration issue for a set of real cell phone data, and ļ¬nd support for a stratiļ¬ed model, where individuals are assigned to one of two distinct groups having diļ¬erent within-group and across-group dynamics
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