Scientists need new tools to explore and browse large collections of scholarly literature. Thanks to organizations such as JSTOR, which scan and index the original bound archives of many journals, modern scientists can search digital libraries spanning hundreds of years. A scientist, suddenly

Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient—steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher’s email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people’s roles. 1 Introduction and Related Work Social network analysis (SNA) is the study of mathematical models for interactions among people, organizations and groups. With the recent availability of large datasets of human

We present the nested Chinese restaurant process (nCRP), a stochastic process which assigns probability distributions to infinitely-deep, infinitely-branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Specifically, we present an application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction. Given a corpus of documents, a posterior inference algorithm finds an approximation to a posterior distribution over trees, topics and allocations of words to levels of the tree. We demonstrate this algorithm on collections of scientific abstracts from several journals. This model exemplifies a recent trend in statistical machine learning—the use of Bayesian nonparametric methods to infer distributions on flexible data structures.

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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.

In recent years, informal, online communication has transformed the ways in which we connect and collaborate with friends and colleagues. With millions of individuals communicating online each day, we have a unique opportunity to observe the formation and evolution of roles and relationships in networked groups and organizations. Yet a number of challenges arise when attempting to infer the underlying social network from data that is often ambiguous, incomplete and context-dependent. In this paper, we consider the problem of collaborative network discovery from domains such as intelligence analysis and litigation support where the analyst is attempting to construct a validated representation of the social network. We specifically address the challenge of relationship identification where the objective is to identify relevant communications that substantiate a given social relationship type. We propose a supervised ranking approach to the problem and assess its performance on a manager-subordinate relationship identification task using the Enron email corpus. By exploiting message content, the ranker routinely cues the analyst to relevant communications relationships and message traffic that are indicative of the social relationship.

Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable.

Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective community recommendations in order to meet more users. In this paper, we investigate two algorithms from very different domains and evaluate their effectiveness for personalized community recommendation. First is association rule mining (ARM), which discovers associations between sets of communities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommendations

We present two approaches to email thread summarization: Collective Message Summarization (CMS) applies a multi-document summarization approach, while Individual Message Summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate these ideas on the Enron collection—a very challenging corpus because of the highly technical language. Experimental results point to two findings: that CMS represents a better approach to email thread summarization, and that current sentence compression techniques do not improve summarization performance in this genre. 1

Abstract. Exposure and adoption of opinions in social networks are important questions in education, business, and government. We describe a novel application of pervasive computing based on using mobile phone sensors to measure and model the face-to-face interactions and subsequent opinion changes amongst undergraduates, during the 2008 US presidential election campaign. We find that self-reported political discussants have characteristic interaction patterns and can be predicted from sensor data. Mobile features can be used to estimate unique individual exposure to different opinions, and help discover surprising patterns of dynamic homophily related to external political events, such as election debates and election day. To our knowledge, this is the first time such dynamic homophily effects have been measured. Automatically estimated exposure explains individual opinions on election day. Finally, we report statistically significant differences in the daily activities of individuals that change political opinions versus those that do not, by modeling and discovering dominant activities using topic models. We find people who decrease their interest in politics are routinely exposed (face-to-face) to friends with little or no interest in politics. 1