Complex networks are structurally distinguishable by domain

Complex networks arise in many domains and often represent phenomena such as brain activity, social relationships, molecular interactions, hyperlinks, and re-tweets. In this work, we study the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from six different network models. We formulate this problem as a multiclass classification problem and learn a model to predict the domain of a new previously unseen network using only a small set of simple structural features. The model is able to accurately predict the domain of arbitrary networks from 17 different domains with 95.7% accuracy. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the graph model used to generate it. Overall, the results demonstrate that networks drawn from different domains and graph models are distinguishable using a few simple structural features.