Is a Sampled Network a Good Enough Descriptor? Missing Links and Appropriate Choice of Representation
(English)Manuscript (preprint) (Other academic)
Missing links due to sampling difficulties can be a limitation in network analysis. Measurements and analysis of networks with insufficient data may make the actual properties indistinct and thus include too much uncertainty to lead to accurate inferences. In addition, in dynamical networks with low link degrees and high stochasticity one sample of the network structure during a finite time window may not be sufficient for general conclusions. Our interest here is to examine the possible consequences of analysis of networks with insufficient data. We studied how mean link degree in sampled networks affects predictions of the spread of disease. Networks with weighted links were used to run scenarios that assumed distance-dependent probabilities of disease transmission when applying general simulation methodology. These scenarios were compared with scenarios including randomly drawn probabilities of disease transmission. For both types of scenarios, we also tested two link-forming methods, one based on distance-dependence and the other on a random approach. Our findings imply that sampled networks must be improved by using statistical measures before attempting to estimate or predict the spread of disease. We conclude that, under the assumption of weighted links, predictions about the extent of an epidemic can be drawn only at mean degrees that are much higher than found in empirical studies. In reality, neither sampling procedures nor disease transmissions are completely dependent on distance. Our results show how this aspect enforces an even higher level of mean degree to be present in order to achieve reasonable predictions.
IdentifiersURN: urn:nbn:se:liu:diva-76374OAI: oai:DiVA.org:liu-76374DiVA: diva2:514146