  In social systems, people communicate with each other and form groups based
on their interests. The pattern of interactions, the network, and the ideas
that flow on the network naturally evolve together. Researchers use simple
models to capture the feedback between changing network patterns and ideas on
the network, but little is understood about the role of past events in the
feedback process. Here we introduce a simple agent-based model to study the
coupling between peoples' ideas and social networks, and better understand the
role of history in dynamic social networks. We measure how information about
ideas can be recovered from information about network structure and, the other
way around, how information about network structure can be recovered from
information about ideas. We find that it is in general easier to recover ideas
from the network structure than vice versa.
