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Surveys conducted on social groups often generate incomplete information due to imperfect response rates. Drawing on Facebook data from a nationally representative sample of graduating college students in Taiwan, we examined the extent to which partial contact records predict which Facebook users belong to a specific class. We first used data from classes with low to middle response rates to train a model for classmate prediction. Based on data from classes with high or perfect response rates, we simulated data by using four different sampling methods with various response rates, and applied the trained model on simulated data to classmate prediction. With a minimal response rate of 40 percent, we achieved an accuracy rate of 90 percent and a true positive rate of 86 percent. Chronological order sampling had the best prediction performance, followed closely by popularity sampling, then by random sampling, and lastly by unpopularity sampling. • Sociological surveys often generate incomplete information. • A machine learning approach to identify members of a specific social group from incomplete social media data (Facebook). • Social media data from 40 % of the group members is sufficient to successfully identify 86 % of the group members. • Social media data from early respondents result in better models for membership prediction. • Social media data from popular members also result in better models for membership prediction. [ABSTRACT FROM AUTHOR]

Abstract Background A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes. Results Enzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance. Conclusion Our analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon originates in mechanisms driving network evolution. Closeness centrality reflects phylogenetic profile poorly. This is because metabolic networks often consist of distinct functional modules and some are not in the centre of the network. Enzymes in these peripheral parts of a network might be important for cell survival and should therefore occur in many bacterial species. They are, however, distant from other enzymes in the same network.