Annual Report 2008.pdf - SAMSI

Harvard University
patrick@seas.harvard.edu
“Sampling Random Networks to Discover Structure: Models and Methods”
Benjamin P. Olding and Patrick J. Wolfe (joint work with Omar Abdala and David Parkes)
Statistics and Information Sciences Laboratory, Harvard University
While the study of random graphs has yielded many classical results, today's vast network data
sets often preclude such direct analysis techniques. In this talk we describe alternative models
and methods to discover structure in random network topology via sampling. Though traditional
models have well-established asymptotic properties and
admit straightforward simulation techniques, they often fail to describe properties of observed
random networks, such as constraints on node linkages. In such scenarios, exact simulation in a
way that respects the underlying measure rapidly becomes difficult, and fewer large-sample
limits are known. We show that stochastic computation can be used to effectively sample from
such spaces and hence estimate functionals of interest. As an example, we sample from a null
model designed for the problem of "community detection" in large graphs—a task closely related
to spectral clustering. We give estimates of the power of this test for several possible models of
topological structure.
Jun Yang
Duke University
junyang@cs.duke.edu
“Data-Driven Processing in Sensor Networks”
Wireless sensor networks enable data collection from the physical environment on
unprecedented scales. In this talk, I will describe some data processing problems that arise in
building an environmental sensing network in Duke Forest, in collaboration with ecologists and
statisticians. Because of severe resource constraints on battery-powered sensor nodes, it is
infeasible to collect and report all raw readings for centralized processing. An effective
approach is model-driven data acquisition, which avoids acquiring readings that can be
accurately predicted from known spatio-temporal models of data. We argue for an alternative,
data-driven approach, which exploits models in optimizing push-based reporting, but does not
depend on the quality of models for correctness. A particularly thorny issue with push-based
reporting is transmission failures, which are common in sensor networks, and make failed reports
indistinguishable from intentionally suppressed ones. The cost of implementing reliable
transmissions is prohibitively high. We show how to inject application-level redundancy in data
reporting to enable efficient, effective, and principled resolution of uncertainty in the missing
data.
Bin Yu
University of California, Berkeley
binyu@stat.berkeley.edu