Network sampling coverage II: The effect of non-random missing data on network measurement.
TLDR
The effect of missing data on network measurement across widely different circumstances is described and it is found that bias is worse when more central nodes are missing and larger, directed networks tend to be more robust, but the relation is weak.About:
This article is published in Social Networks.The article was published on 2017-01-01 and is currently open access. It has received 110 citations till now. The article focuses on the topics: Missing data & Imputation (statistics).read more
Citations
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The application of statistical network models in disease research
Matthew J. Silk,Darren P. Croft,Richard J. Delahay,David J. Hodgson,Nicola Weber,Mike Boots,Mike Boots,Robbie A. McDonald +7 more
TL;DR: It is argued that developments in the statistical analysis of empirical contact data are critical given the ready availability of dynamic network data from bio‐logging studies, and improved integration of statistical network approaches into epidemiological research is encouraged to facilitate the generation of novel modelling frameworks and help extend the understanding of disease transmission in natural populations.
Journal ArticleDOI
Subgraph Robustness of Complex Networks Under Attacks
TL;DR: By introducing the subgraph robustness problem, this work develops analytically a framework to investigate robustness properties of the two types of subgraphs under random attacks, localized attacks, and targeted attacks and finds that the benchmark models, such as Erdős-Rényi graphs, random regular networks, and scale-free networks possess distinct characteristic subgraph resilient features.
Journal ArticleDOI
Understanding animal social structure: exponential random graph models in animal behaviour research
Matthew J. Silk,David N. Fisher +1 more
TL;DR: In this paper, exponential random graph models (ERGMs) have been used to model the formation of network links, which have great potential for modelling animal social structure. But, the authors highlight the strengths and weaknesses of this approach relative to more conventional methods, and provide some guidance on the situations and research areas in which they can be used appropriately.
Journal ArticleDOI
Analysing animal social network dynamics: the potential of stochastic actor-oriented models.
TL;DR: A review of the recent applications of stochastic actor‐oriented models to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks.
Proceedings ArticleDOI
Missing network data a comparison of different imputation methods
TL;DR: This paper compares several imputation methods for missing data in network analysis on a diverse set of simulated networks under several missing data mechanisms to indicate that the default methods do not perform well with moderate or large amounts of missing data.
References
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