  Community detection algorithms are fundamental tools to understand
organizational principles in social networks. With the increasing power of
social media platforms, when detecting communities there are two possi- ble
sources of information one can use: the structure of social network and node
attributes. However structure of social networks and node attributes are often
interpreted separately in the research of community detection. When these two
sources are interpreted simultaneously, one common as- sumption shared by
previous studies is that nodes attributes are correlated with communities. In
this paper, we present a model that is capable of combining topology
information and nodes attributes information with- out assuming correlation.
This new model can recover communities with higher accuracy even when node
attributes and communities are uncorre- lated. We derive the detectability
threshold for this model and use Belief Propagation (BP) to make inference.
This algorithm is optimal in the sense that it can recover community all the
way down to the threshold. This new model is also with the potential to handle
edge content and dynamic settings.
