We know who you followed last summer

It is concluded from this analysis that real-world events and changes to Twitter's interface for recommending users strongly influence network growth, and a single static snapshot can give novel insights about Twitter's evolution.

Abstract

Understanding a network's temporal evolution appears to require multiple observations of the graph over time. These often expensive repeated crawls are only able to answer questions about what happened from observation to observation, and not what happened before or between network snapshots. Contrary to this picture, we propose a method for Twitter's social network that takes a single static snapshot of network edges and user account creation times to accurately infer when these edges were formed. This method can be exact in theory, and we demonstrate empirically for a large subset of Twitter relationships that it is accurate to within a few hours in practice.