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 it is accurate to within hours in practice.
We study users who have a very large number of edges or who are recommended by Twitter. We examine the graph formed by these nearly 1,800 Twitter celebrities and their 862 million edges in detail, showing that a single static snapshot can give novel insights about Twitter’s evolution. We conclude from this analysis that real-world events and changes to Twitter’s interface for recommending users strongly inﬂuence network growth.