Title: Temporally Evolving Community Detection and Prediction in Content-Centric Networks,
Abstract: In this work, we consider the problem of combining link, content and temporal
analysis for community detection and prediction in evolving networks. Such
temporal and content-rich networks occur in many real-life settings, such as
bibliographic networks and question answering forums. Most of the work in the
literature (that uses both content and structure) deals with static snapshots
of networks, and they do not reflect the dynamic changes occurring over
multiple snapshots. Incorporating dynamic changes in the communities into the
analysis can also provide useful insights about the changes in the network such
as the migration of authors across communities. In this work, we propose
Chimera, a shared factorization model that can simultaneously account for graph
links, content, and temporal analysis. This approach works by extracting the
latent semantic structure of the network in multidimensional form, but in a way
that takes into account the temporal continuity of these embeddings. Such an
approach simplifies temporal analysis of the underlying network by using the
embedding as a surrogate. A consequence of this simplification is that it is
also possible to use this temporal sequence of embeddings to predict future
communities. We present experimental results illustrating the effectiveness of
the approach.