Computerized social networks have a benefit that is derived from large size. Larger sized networks, such as Facebook, have a significant number of users making a significant number of interactions with other users and content items on the network to provide the operators of the network with social data that can reasonably approach a point of meaningful information that can then be utilized to present to its users recommendations, item listings, and other information based on how other users in the network have behaved. Smaller networks and e-commerce systems typically do not have the volume of users or user interactions to effectively generate their own meaningful data. Additionally, the topology, homophilous nature, and dimensionality of the structure of the users and network may be insufficient for a desired social signal. Further, real users can provide additional problems, such as difficulty in building a critical mass, scaling problems with too many users, users not interacting with consistent regularity, too many similar users (which results in something like over-fitting in a machine learning application), and/or other problems.