The link prediction problem is an emerging real-life social network problem in which data mining techniques have played a critical role. It arises in many practical applications such as recommender systems, information retrieval, and marketing analysis of social networks. We propose a new mathematical programming approach for predicting a future network using estimated node degree distribution identified from historical data. The link prediction problem is formulated as an integer programming problem that maximizes the sum of link scores (probabilities) with respect to the estimated node degree distribution. The performance of the proposed framework is tested on real-life social networks, and the computational results show that the proposed approach can improve the performance of previously published link prediction methods.

The existing Science Citation Index only counts direct citations, whereas PageRank disregards the number of direct citations. We propose a new Comprehensive Citation Index (CCI) that evaluates both direct and indirect intellectual influence of research papers, and show that CCI is more reliable in discovering research papers with far-reaching influence.

The ability to predict linkages among data objects is central to many data mining tasks, such as product recommendation and social network analysis. Substantial literature has been devoted to the link prediction problem either as an implicitly embedded problem in specific applications or as a generic data mining task. This literature has mostly adopted a static graph representation where a snapshot of the network is analyzed to predict hidden or future links. However, this representation is only appropriate to investigate whether a certain link will ever occur and does not apply to many applications for which the prediction of the repeated link occurrences are of primary interest (e.g., communication network surveillance). In this paper, we introduce the time-series link prediction problem, taking into consideration temporal evolutions of link occurrences to predict link occurrence probabilities at a particular time. Using Enron e-mail data and high-energy particle physics literature coauthorship data, we have demonstrated that time-series models of single-link occurrences achieve comparable link prediction performance with commonly used static graph link prediction algorithms. Furthermore, a combination of static graph link prediction algorithms and time-series models produced significantly better predictions over static graph link prediction methods, demonstrating the great potential of integrated methods that exploit both interlink structural dependencies and intralink temporal dependencies.

This paper estimates component reliability from masked series-system life data, viz, data where the exact component causing system failure might be unknown. It focuses on a Bayes approach which considers prior information on the component reliabilities. In most practical settings, prior engineering knowledge on component reliabilities is extensive. Engineers routinely use prior knowledge and judgment in a variety of ways. The Bayes methodology proposed here provides a formal, realistic means of incorporating such subjective knowledge into the estimation process. In the event that little prior knowledge is available, conservative or even noninformative priors, can be selected. The model is illustrated for a 2-component series system of exponential components. In particular it uses discrete-step priors because of their ease of development and interpretation. By taking advantage of the prior information, the Bayes point-estimates consistently perform well, i.e., are close to the MLE. While the approach is computationally intensive, the calculations can be easily computerized.