Effects of missing data in social networks

@article{Kossinets2006EffectsOM,
  title={Effects of missing data in social networks},
  author={Gueorgi Kossinets},
  journal={Soc. Networks},
  year={2006},
  volume={28},
  pages={247-268}
}
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796 Citations
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796 Citations

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References

SHOWING 1-10 OF 84 REFERENCES
Missing data in networks: exponential random graph (p∗) models for networks with non-respondents
NETWORK DATA AND MEASUREMENT
TLDR
Continued research on data quality is needed; beyond improved samples and further investigation of the informant accuracy/reliability issue, this should cover common indices of network structure, address the consequences of sampling portions of a network, and examine the robustness of indicators ofnetwork structure and position to both random and nonrandom errors of measurement.
Mixing patterns in networks.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2003
TLDR
This work proposes a number of measures of assortative mixing appropriate to the various mixing types, and applies them to a variety of real-world networks, showing that assortsative mixing is a pervasive phenomenon found in many networks.
Community structure in social and biological networks
  • M. Girvan, M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
TLDR
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Social Network Analysis: Methods and Applications
TLDR
This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of dyadic and triadic interaction models, which provide insights into the structure and dynamics of relationships between actors and groups.
A note on missing network data in the general social survey
Ego-centered networks and the ripple effect
Network robustness and fragility: percolation on random graphs.
TLDR
This paper studies percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolators, bond percolations, and models in which occupation probabilities depend on vertex degree.
Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions
Networks of several distinct types of social tie are aggregated by a dual model that partitions a population while simultaneously identifying patterns of relations. Concepts and algorithms are…
Network inference, error, and informant (in)accuracy: a Bayesian approach
  • C. Butts
  • Computer Science
    Soc. Networks
  • 2003
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