  We present a hybrid method for latent information discovery on the data sets
containing both text content and connection structure based on constrained low
rank approximation. The new method jointly optimizes the Nonnegative Matrix
Factorization (NMF) objective function for text clustering and the Symmetric
NMF (SymNMF) objective function for graph clustering. We propose an effective
algorithm for the joint NMF objective function, based on a block coordinate
descent (BCD) framework. The proposed hybrid method discovers content
associations via latent connections found using SymNMF. The method can also be
applied with a natural conversion of the problem when a hypergraph formulation
is used or the content is associated with hypergraph edges.
  Experimental results show that by simultaneously utilizing both content and
connection structure, our hybrid method produces higher quality clustering
results compared to the other NMF clustering methods that uses content alone
(standard NMF) or connection structure alone (SymNMF). We also present some
interesting applications to several types of real world data such as citation
recommendations of papers. The hybrid method proposed in this paper can also be
applied to general data expressed with both feature space vectors and pairwise
similarities and can be extended to the case with multiple feature spaces or
multiple similarity measures.
