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Link Analysis has been a popular and widely used Web mining technique, especially in the area of Web search. Various ranking schemes based on link analysis have been proposed, of which the PageRank metric has gained the most popularity with the success of Google. Over the last few years, there has been significant work in improving the relevance model of PageRank to address issues such as personalization and topic relevance. In addition, a variety of ideas have been proposed to address the computational aspects of PageRank, both in terms of efficient I/O computations and matrix computations involved in computing the PageRank score. The key challenge has been to perform computation on very large Web graphs. In this paper, we propose a method to incrementally compute PageRank for a large graph that is evolving. We note that although the Web graph evolves over time, its rate of change is rather slow. When compared to its size. We exploit the underlying principle of first order markov model on which PageRank is based, to incrementally compute PageRank for the evolving Web graph. Our experimental results show significant speed up in computational cost, the computation involves only the (small) portion of Web graph that has undergone change. Our approach is quite general, and can be used to incrementally compute (on evolving graphs) any metric that satisfies the first order Markov property.

Abstract-This paper presents a generalized version of the linear threshold model for simulating multiple cascades on a network while allowing nodes to switch between them. The proposed model is shown to be a rapidly mixing Markov chain and the corresponding steady state distribution is used to estimate highly likely states of the cascades' spread in the network. Results on a variety of real world networks demonstrate the high quality of the estimated solution.

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Advanced communication technologies enable strangers to work together on the same tasks or projects in virtual environments. Understanding the formation of taskoriented groups is an important first step to study the dynamics of team collaboration. In this paper, we investigated group combat activities in Sony's EverQuest II game to identify the role of player and group attributes on group formation. We found that group formation is highly influenced by players' common interests on challenging tasks. Players with less combat experience are more likely to participate in group events for difficult tasks and team performance is positively correlated to group size.

We present a case study of a pilot project that was developed to evaluate the use of data mining in audit selection for the Minnesota Department of Revenue (DOR). The Internal Revenue Service (IRS) estimated the gap between revenue owed and revenue collected for 2001 to be approximately $345 billion, of which they were able to recover only $55 billion, and the estimated gap for 2006 was approximately $450 billion, of which the IRS was able to recover only $65 billion. It is critical for the government to reduce the gap and the fundamental process for doing so is audit selection. We present a data mining based approach that was used to improve the audit selection process at the DOR. We describe the manual audit selection process used at the time of the pilot project for Sales and Use taxes, discuss the data from various sources, address issues regarding feature selection, and explain the data mining techniques used. Results from the pilot project revealed that the data mining based approach can increase efficiency in the audit selection process. We also report results from actual field audits performed by auditors at the DOR, and results validated the usefulness of the data mining based approach for audit selection. The impact of the pilot project would be a refinement of the manual audit selection process and tax assessment procedures for other types of taxes.

Health records of any type should be confidential, but simultaneously they should be accessible to an authorized user. In the current conventional health care system, health records are processed in the local database. Only authorized hospital staff can have access to the patient health records kept in health centers. The flow of information in this kind of system is center-restricted. One technology that can potentially overcome this problem is Blockchain. Blockchain technology allows sharing of data in a distributed way with benefits like privacy-preserving and secure access control. To achieve secure access control and privacy of electronic healthcare data (EHD), a secure framework for distributed sharing of the health record is proposed. The use of blockchain will remove the requirement of a trusted 3rd party for data storage. The framework uses searchable key attribute-based encryption (ABE) for achieving fine-grain access control. The simulation results indicate that our scheme is quite effective and secure.