A general solution to the preferential selection model
This provides a foundational solution for modeling preferential selection in systems like social networks, though it is incremental as it builds on existing models.
The authors derived a general analytic solution to Herbert Simon's 1955 model for time-evolving novelty functions, which serves as a precursor to preferential attachment models in social networks, and demonstrated that systems modeled as occurrence data can be generatively simulated with exceptionally high accuracy.
We provide a general analytic solution to Herbert Simon's 1955 model for time-evolving novelty functions. This has far-reaching consequences: Simon's is a pre-cursor model for Barabasi's 1999 preferential attachment model for growing social networks, and our general abstraction of it more considers attachment to be a form of link selection. We show that any system which can be modeled as instances of types---i.e., occurrence data (frequencies)---can be generatively modeled (and simulated) from a distributional perspective with an exceptionally high-degree of accuracy.