837 research outputs found
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms
Age-Optimal Updates of Multiple Information Flows
In this paper, we study an age of information minimization problem, where
multiple flows of update packets are sent over multiple servers to their
destinations. Two online scheduling policies are proposed. When the packet
generation and arrival times are synchronized across the flows, the proposed
policies are shown to be (near) optimal for minimizing any time-dependent,
symmetric, and non-decreasing penalty function of the ages of the flows over
time in a stochastic ordering sense
Seborrheic dermatitis
This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of seborrheic dermatitis
A Co-evolution Framework towards Stable Designs from Radical Innovations for Organizations Using IT
The purpose of this paper is to theoretically and empirically explore how organizations can enable radical product innovations to cumulate as stable designs. Radical product innovations are organizational responses to external triggers that cause transitions. To manage in transitions, it is necessary for radical product innovations to cumulate as stable designs. Organizations ability to co-evolve with the environment does influence innovations to cumulate as stable designs; to examine this, the author selected public procurement that uses IT as radical product innovation with pronounced environmental influence, government’s interventionist approach. The author used multiple case-study and obtained diverse analytic and heuristic views. From the cases, the author noted that actors did consider local and contingent factors only that resulted in certain radical innovations cumulating as stable designs. As an initial starting point, such actions are appropriate but organizational actions to expand their initial actions with a co-evolutionary framework that considers social contexts
Graph Sample and Hold: A Framework for Big-Graph Analytics
Sampling is a standard approach in big-graph analytics; the goal is to
efficiently estimate the graph properties by consulting a sample of the whole
population. A perfect sample is assumed to mirror every property of the whole
population. Unfortunately, such a perfect sample is hard to collect in complex
populations such as graphs (e.g. web graphs, social networks etc), where an
underlying network connects the units of the population. Therefore, a good
sample will be representative in the sense that graph properties of interest
can be estimated with a known degree of accuracy. While previous work focused
particularly on sampling schemes used to estimate certain graph properties
(e.g. triangle count), much less is known for the case when we need to estimate
various graph properties with the same sampling scheme. In this paper, we
propose a generic stream sampling framework for big-graph analytics, called
Graph Sample and Hold (gSH). To begin, the proposed framework samples from
massive graphs sequentially in a single pass, one edge at a time, while
maintaining a small state. We then show how to produce unbiased estimators for
various graph properties from the sample. Given that the graph analysis
algorithms will run on a sample instead of the whole population, the runtime
complexity of these algorithm is kept under control. Moreover, given that the
estimators of graph properties are unbiased, the approximation error is kept
under control. Finally, we show the performance of the proposed framework (gSH)
on various types of graphs, such as social graphs, among others
Implementation of Distributed Time Exchange Based Cooperative Forwarding
In this paper, we design and implement time exchange (TE) based cooperative
forwarding where nodes use transmission time slots as incentives for relaying.
We focus on distributed joint time slot exchange and relay selection in the sum
goodput maximization of the overall network. We formulate the design objective
as a mixed integer nonlinear programming (MINLP) problem and provide a
polynomial time distributed solution of the MINLP. We implement the designed
algorithm in the software defined radio enabled USRP nodes of the ORBIT indoor
wireless testbed. The ORBIT grid is used as a global control plane for exchange
of control information between the USRP nodes. Experimental results suggest
that TE can significantly increase the sum goodput of the network. We also
demonstrate the performance of a goodput optimization algorithm that is
proportionally fair.Comment: Accepted in 2012 Military Communications Conferenc
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