Event-based services have recently witnessed a rapid growth driving the way people explore and share information of interest. They host a huge amount of users' activities including explicit RSVP, shared photos, comments and social connections. Exploiting these activities to detect communities of similar users is a challenging problem. In reality, a community in event-based social network (ESBN) is a group of users not only sharing common events and friends, but also having similar topical interests. However, such community could not be detected by most of existing methods which mainly draw on link analysis in the network. To address this problem, there is a need to capitalize on the semantics of shared objects along with the structural properties, and to generate overlapping communities rather than disjoint ones. In this paper, we propose to leverage the users' activities around events with the aim to detect communities based on topical clustering and link analysis that maximize a new form of semantic modularity. We particularly highlight the difference between online and offline social interactions, and the influence of event categories to detect communities. Experimental results on real datasets showed that our approach was able to detect semantically meaningful communities compared with existing state of the art methods.
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer. This paper tackles the problem of detecting topical communities from within an organization by mining readily available network access pattern information. A Bayesian generative process is used to model the behavior of user's network access pattern and thereby her consumption of online content. The idea is that users within same topical interest group tend to share similar online access patterns. By leveraging this pattern, along with side information of domain-names and keywords within the accessed websites, one is able to model these observations under the framework of a mixed membership statistical model. Hence the access patterns of users-to-websites, as measured at the edge of an organization's network boundary, can be decomposed into constituent topical communities without any human effort in selecting specific features. Experimental results on real-world network flow trace demonstrate that the proposed method can effectively detect topically meaningful community structures. Besides better detection accuracy of communities compared with other community detection methods, the proposed method can detect interesting but non-evident hidden communities which cannot readily be detected by other known methods. The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities. Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous. We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.