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Title: Workshop on Incomplete Network Data Held at Sandia National Labs – Livermore

While network analysis is applied in a broad variety of scientific fields (including physics, computer science, biology, and the social sciences), how networks are constructed and the resulting bias and incompleteness have drawn more limited attention. For example, in biology, gene networks are typically developed via experiment -- many actual interactions are likely yet to be discovered. In addition to this incompleteness, the data-collection processes can introduce significant bias into the observed network datasets. For instance, if you observe part of the World Wide Web network through a classic random walk, then high degree nodes are more likely to be found than if you had selected nodes at random. Unfortunately, such incomplete and biasing data collection methods must be often used.
Authors:
 [1] ;  [2]
  1. Syracuse Univ., NY (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
OSTI Identifier:
1259543
Report Number(s):
SAND2016--5324R
641383
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Research Org:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING