Getting an overview of a large online social net-work and deciding which communities to join is a challenging task for a new user. We propose a method that maps a large network into a smaller graph with two kinds of nodes: a node of the first kind is representative of a community, a node of the second kind is neighbor to a representative and rejects the semantics of that community. Our approach encompasses a learning and ranking algorithm that derives this smaller graph from the original one, and a visualization algorithm that returns a graph layout to the observer. We report on our results on inspecting the network of a folksonomy.

Probabilistic latent semantic indexing (PLSI) represents documents of a collection as mixture proportions of latent topics, which are learned from the collection by an expectation maximization (EM) algorithm. New documents or queries need to be folded into the latent topic space by a simplified version of the EM-algorithm. During PLSI- Folding-in of a new document, the topic mixtures of the known documents are ignored. This may lead to a suboptimal model of the extended collection. Our new approach incorporates the topic mixtures of the known documents in a Bayesian way during folding- in. That knowledge is modeled as prior distribution over the topic simplex using a kernel density estimate of Dirichlet kernels. We demonstrate the advantages of the new Bayesian folding-in using real text data.