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 .

• fcnn/ contains the code for training the FCNN

• preprocessors/ code for parsing and extracting features from the pokec profile data

• load_dataset.R code to combine graph embedding features and pokec profile data. code for preparing the dataset (e.g. outliers, normalization)

• prediction.R models for predicting age and evaluation

• per_class_metric.R MPA_MAE (see report)

• correlation_analysis.R basic correlation and R^2

• 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