Abstract. In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data as link reidentification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.

Presence-absence (0-1) observations are special in that often the absence of evidence is not evidence of absence. Here we develop an independent factor model, which has the unique capability to isolate the former as an independent discrete binary noise factor. This representation then forms the basis of inferring missed presences by means of denoising. This is achieved in a probabilistic formalism, employing independent Beta latent source densities and a Bernoulli data likelihood model. Variational approximations are employed to make the inferences tractable. We relate our model to existing models of 0-1 data, demonstrating its advantages for the problem considered, and we present applications in several problem domains, including social network analysis and DNA fingerprint analysis. Key words: factor models, data denoising, 0-1 data 1

This paper proposes a novel extractive summarization method for contact center dialogues. We use a particular type of hidden Markov model (HMM) called Class Speaker HMM (CSHMM), which processes operator/caller utterance sequences of multiple domains simultaneously to model domain-specific utterance sequences and common (domainwide) sequences at the same time. We applied the CSHMM to call summarization of transcripts in six different contact center domains and found that our method significantly outperforms competitive baselines based on the maximum coverage of important words using integer linear programming. 1

and the National Science Foundation (PSLC) under contract no. SBE-0354420. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: cognitive models, intelligent tutoring systems, machine learning, educational data mining, learning factors, psychometrics, additive factor models, latent variable models, exponential principal component analysis, logistic regression, combinatorial search ii To my parents and to my wife iii CONTENTS Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning........................................................................................................................ i