From the raw information like profile features and friendship relations we evaluate which features, if revealed, pose a risk to user privacy in the context of age. We perform experiments on a real world online social network and predict the age of users with an average error of 2.62 years, 36% better than previous work.
The full project report .
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fcnn/ contains the code for training the FCNN
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preprocessors/ code for parsing and extracting features from the pokec profile data
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load_dataset.R code to combine graph embedding features and pokec profile data. code for preparing the dataset (e.g. outliers, normalization)
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prediction.R models for predicting age and evaluation
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per_class_metric.R MPA_MAE (see report)
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correlation_analysis.R basic correlation and R^2
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outliers.R experiments to identify outliers
What is not contained:
- histogram generation (R gives this in one line)
- backward selection (we used the code from prediction with a loop removing each feature, one after the other, one at a time)
- node2vec code, see external code section
Remarks: Most of the files contain one or several path variables which refer to the location of the data. These must be adjusted accordingly.
Dataset: https://snap.stanford.edu/data/soc-Pokec.html
External Code: https://github.com/snap-stanford/snap/tree/master/examples/node2vec we just applied the code as it is to the relationship graph