3,801 research outputs found
Moment based estimation of stochastic Kronecker graph parameters
Stochastic Kronecker graphs supply a parsimonious model for large sparse real
world graphs. They can specify the distribution of a large random graph using
only three or four parameters. Those parameters have however proved difficult
to choose in specific applications. This article looks at method of moments
estimators that are computationally much simpler than maximum likelihood. The
estimators are fast and in our examples, they typically yield Kronecker
parameters with expected feature counts closer to a given graph than we get
from KronFit. The improvement was especially prominent for the number of
triangles in the graph.Comment: 22 pages, 4 figure
Convergence Rate Analysis of Distributed Gossip (Linear Parameter) Estimation: Fundamental Limits and Tradeoffs
The paper considers gossip distributed estimation of a (static) distributed
random field (a.k.a., large scale unknown parameter vector) observed by
sparsely interconnected sensors, each of which only observes a small fraction
of the field. We consider linear distributed estimators whose structure
combines the information \emph{flow} among sensors (the \emph{consensus} term
resulting from the local gossiping exchange among sensors when they are able to
communicate) and the information \emph{gathering} measured by the sensors (the
\emph{sensing} or \emph{innovations} term.) This leads to mixed time scale
algorithms--one time scale associated with the consensus and the other with the
innovations. The paper establishes a distributed observability condition
(global observability plus mean connectedness) under which the distributed
estimates are consistent and asymptotically normal. We introduce the
distributed notion equivalent to the (centralized) Fisher information rate,
which is a bound on the mean square error reduction rate of any distributed
estimator; we show that under the appropriate modeling and structural network
communication conditions (gossip protocol) the distributed gossip estimator
attains this distributed Fisher information rate, asymptotically achieving the
performance of the optimal centralized estimator. Finally, we study the
behavior of the distributed gossip estimator when the measurements fade (noise
variance grows) with time; in particular, we consider the maximum rate at which
the noise variance can grow and still the distributed estimator being
consistent, by showing that, as long as the centralized estimator is
consistent, the distributed estimator remains consistent.Comment: Submitted for publication, 30 page
Learning multifractal structure in large networks
Generating random graphs to model networks has a rich history. In this paper,
we analyze and improve upon the multifractal network generator (MFNG)
introduced by Palla et al. We provide a new result on the probability of
subgraphs existing in graphs generated with MFNG. From this result it follows
that we can quickly compute moments of an important set of graph properties,
such as the expected number of edges, stars, and cliques. Specifically, we show
how to compute these moments in time complexity independent of the size of the
graph and the number of recursive levels in the generative model. We leverage
this theory to a new method of moments algorithm for fitting large networks to
MFNG. Empirically, this new approach effectively simulates properties of
several social and information networks. In terms of matching subgraph counts,
our method outperforms similar algorithms used with the Stochastic Kronecker
Graph model. Furthermore, we present a fast approximation algorithm to generate
graph instances following the multi- fractal structure. The approximation
scheme is an improvement over previous methods, which ran in time complexity
quadratic in the number of vertices. Combined, our method of moments and fast
sampling scheme provide the first scalable framework for effectively modeling
large networks with MFNG
Moment-based parameter estimation in binomial random intersection graph models
Binomial random intersection graphs can be used as parsimonious statistical
models of large and sparse networks, with one parameter for the average degree
and another for transitivity, the tendency of neighbours of a node to be
connected. This paper discusses the estimation of these parameters from a
single observed instance of the graph, using moment estimators based on
observed degrees and frequencies of 2-stars and triangles. The observed data
set is assumed to be a subgraph induced by a set of nodes sampled from
the full set of nodes. We prove the consistency of the proposed estimators
by showing that the relative estimation error is small with high probability
for . As a byproduct, our analysis confirms that the
empirical transitivity coefficient of the graph is with high probability close
to the theoretical clustering coefficient of the model.Comment: 15 pages, 6 figure
Analysis and Approximate Inference of Large Random Kronecker Graphs
Random graph models are playing an increasingly important role in various
fields ranging from social networks, telecommunication systems, to physiologic
and biological networks. Within this landscape, the random Kronecker graph
model, emerges as a prominent framework for scrutinizing intricate real-world
networks. In this paper, we investigate large random Kronecker graphs, i.e.,
the number of graph vertices is large. Built upon recent advances in random
matrix theory (RMT) and high-dimensional statistics, we prove that the
adjacency of a large random Kronecker graph can be decomposed, in a spectral
norm sense, into two parts: a small-rank (of rank ) signal matrix
that is linear in the graph parameters and a zero-mean random noise matrix.
Based on this result, we propose a ``denoise-and-solve'' approach to infer the
key graph parameters, with significantly reduced computational complexity.
Experiments on both graph inference and classification are presented to
evaluate the our proposed method. In both tasks, the proposed approach yields
comparable or advantageous performance, than widely-used graph inference (e.g.,
KronFit) and graph neural net baselines, at a time cost that scales linearly as
the graph size .Comment: 27 pages, 5 figures, 2 table
- …