  Community structure in networks has been investigated from many viewpoints,
usually with the same end result: a community detection algorithm of some kind.
Recent research offers methods for combining the results of such algorithms
into timelines of community evolution. This paper investigates community
detection and tracking from the data fusion perspective. We avoid the kind of
hard calls made by traditional community detection algorithms in favor of
retaining as much uncertainty information as possible. This results in a method
for directly estimating the probabilities that pairs of nodes are in the same
community. We demonstrate that this method is accurate using the LFR testbed,
that it is fast on a number of standard network datasets, and that it is has a
variety of uses that complement those of standard, hard-call methods. Retaining
uncertainty information allows us to develop a Bayesian filter for tracking
communities. We derive equations for the full filter, and marginalize it to
produce a potentially practical version. Finally, we discuss closures for the
marginalized filter and the work that remains to develop this into a
principled, efficient method for tracking time-evolving communities on
time-evolving networks.
