Title: Network Classification and Categorization,
Abstract: To the best of our knowledge, this paper presents the first large-scale study
that tests whether network categories (e.g., social networks vs. web graphs)
are distinguishable from one another (using both categories of real-world
networks and synthetic graphs). A classification accuracy of $94.2\%$ was
achieved using a random forest classifier with both real and synthetic
networks. This work makes two important findings. First, real-world networks
from various domains have distinct structural properties that allow us to
predict with high accuracy the category of an arbitrary network. Second,
classifying synthetic networks is trivial as our models can easily distinguish
between synthetic graphs and the real-world networks they are supposed to
model.