Network Classification and Categorization
This work addresses the challenge of network categorization for researchers and practitioners, providing insights into structural properties and synthetic model limitations, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of distinguishing network categories, such as social networks versus web graphs, by conducting a large-scale study with real-world and synthetic networks, achieving a classification accuracy of 94.2% using a random forest classifier.
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.