James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, Nesreen K. Ahmed, Ryan A. Rossi, Karl R. B. Schmitt, Sucheta Soundarajan

To the best of our knowledge, this paper presents the first large-scale studythat tests whether network categories (e.g., social networks vs. web graphs)are distinguishable from one another (using both categories of real-worldnetworks and synthetic graphs). A classification accuracy of $94.2\%$ wasachieved using a random forest classifier with both real and syntheticnetworks. This work makes two important findings. First, real-world networksfrom various domains have distinct structural properties that allow us topredict with high accuracy the category of an arbitrary network. Second,classifying synthetic networks is trivial as our models can easily distinguishbetween synthetic graphs and the real-world networks they are supposed tomodel.