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The nature of the Blogosphere determines that the majority of bloggers are only connected with a small number of fellow bloggers, and similar bloggers can be largely disconnected from each other. Aggregating them allows for cost-effective personalized services, targeted marketing, and exploration of new business opportunities. As most bloggers have only a small number of adjacent bloggers, the problem of aggregating similar bloggers presents challenges that demand novel algorithms of connecting the non-adjacent due to the fragmented distributions of bloggers. In this work, we define the problem, delineate its challenges, and present an approach that uses innovative ways to employ contextual information and collective wisdom to aggregate similar bloggers. A real-world blog directory is used for experiments. We demonstrate the efficacy of our approach, report findings, and discuss related issues and future work.

introduction Share on Introduction to special issue on social computing, behavioral modeling, and prediction Editors: Huan Liu View Profile , John Salerno View Profile , Michael Young View Profile , Rakesh Agrawal View Profile , Philip S. Yu View Profile Authors Info & Claims ACM Transactions on Knowledge Discovery from DataVolume 3Issue 2April 2009 Article No.: 6pp 1–3https://doi.org/10.1145/1514888.1514889Published:21 April 2009Publication History 6citation751DownloadsMetricsTotal Citations6Total Downloads751Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The Internet has become a way of life. In the past, when someone wanted to perform research he or she would go to the library, and proceed to the card catalogue to locate a book or to a set of periodicals for magazines. Today we sit in front of our computer, launch our Internet browser, bring up a search engine, and perform various searches by entering a simple keyword, phrase, or more complex Boolean expression. The Internet can be a great asset by significantly cutting the time-consuming burden of finding relevant documents/papers; however, how do we know where and what we are searching? How many times do we perform a query and get irrelevant documents? In this article we investigate today's search engines, what is meant by coverage, and what metasearch engines bring to the table. We also look at both their abilities and deficiencies and present a capability that attempts to put more power at the finger tips of the user. This capability is what we call “Buddy.” © 2008 Wiley Periodicals, Inc.

A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes) and its horizonal list of attributes (or terms). In a text-dominant environment, a topic taxonomy can be used to flexibly describe a group's interests with varying granularity. However, the stagnant nature of a taxonomy may fail to timely capture the dynamic change of a group's interest. This article addresses the problem of how to adapt a topic taxonomy to the accumulated data that reflects the change of a group's interest to achieve dynamic group profiling. We first discuss the issues related to topic taxonomy. We next formulate taxonomy adaptation as an optimization problem to find the taxonomy that best fits the data. We then present a viable algorithm that can efficiently accomplish taxonomy adaptation. We conduct extensive experiments to evaluate our approach's efficacy for group profiling, compare the approach with some alternatives, and study its performance for dynamic group profiling. While pointing out various applications of taxonomy adaption, we suggest some future work that can take advantage of burgeoning Web 2.0 services for online targeted marketing, counterterrorism in connecting dots, and community tracking.

Since its conception the JDL model has provided the fusion community with a way to describe and understand various activities in the data fusion community. As the community continues to make great strides and advancements, primarily in the Level 2/3 arena, a greater understanding is coming about. As a result it is time to revisit the model and to incorporate these lessons learned. In this paper we look back at past accomplishments as a basis to discuss where we might further advance in the next decade toward higher levels of fusion. In doing so we propose refinements to the existing definitions and model of the various levels (as defined by the Joint Director of Laboratories, JDL) and based on these definitions, discuss how each of these levels interact.

Process refinement or feedback is a key component of any closed loop system. Fusion models are no exception. In this paper we explore what process refinement means in terms of the higher levels of fusion. In doing so we further refine the existing definitions of the various levels (as defined by the joint director of laboratories, JDL) and based on these definitions we discuss how each of these levels interacts with each other

In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.

There has been much work done in the use of neural networks to model an existing problem, but little has been done to address what happens after training has been completed and the model must continue to learn new information. How well does the model work on information that it has not seen before? How does it adapt to new information? In this paper we address these issues, beginning our discussion with a neural model that has been trained on parsing simple natural language phrases and how well the model can generalize. Based on these results we then investigate two techniques which attempt to allow the model to "grow" or learn information that it has never before seen.