TY - CONF T1 - Group-in-a-Box Layout for Multi-faceted Analysis of Communities T2 - Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom) Y1 - 2011 A1 - Rodrigues,E.M. A1 - Milic-Frayling,N. A1 - Smith,M. A1 - Shneiderman, Ben A1 - Hansen,D. KW - Algorithm design and analysis KW - category based social graph partitions KW - clustered graphs KW - clustering KW - Clustering algorithms KW - Communities KW - data visualisation KW - force-directed KW - gender KW - geographic location KW - graph layout algorithms KW - graph theory KW - group-in-a-box KW - group-in-a-box layout KW - Image edge detection KW - Layout KW - meta-layout KW - multifaceted community analysis KW - network subgraph visualization KW - network visualization KW - pattern clustering KW - profession KW - semantic substrates KW - Social network services KW - social networking (online) KW - social networks KW - treemap space filling technique KW - Visualization AB - Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets. JA - Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom) PB - IEEE SN - 978-1-4577-1931-8 M3 - 10.1109/PASSAT/SocialCom.2011.139 ER - TY - CONF T1 - Rigorous Probabilistic Trust-Inference with Applications to Clustering T2 - IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09 Y1 - 2009 A1 - DuBois,Thomas A1 - Golbeck,Jennifer A1 - Srinivasan, Aravind KW - Clustering algorithms KW - Conferences KW - Educational institutions KW - Extraterrestrial measurements KW - Inference algorithms KW - Intelligent agent KW - random graphs KW - Social network services KW - trust inferrence KW - Visualization KW - Voting KW - Web sites AB - The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results. JA - IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09 PB - IEEE VL - 1 SN - 978-0-7695-3801-3 M3 - 10.1109/WI-IAT.2009.109 ER - TY - JOUR T1 - Unsupervised learning applied to progressive compression of time-dependent geometry JF - Computers & Graphics Y1 - 2005 A1 - Baby,Thomas A1 - Kim,Youngmin A1 - Varshney, Amitabh KW - Clustering algorithms KW - Distributed/network graphics KW - pattern recognition AB - We propose a new approach to progressively compress time-dependent geometry. Our approach exploits correlations in motion vectors to achieve better compression. We use unsupervised learning techniques to detect good clusters of motion vectors. For each detected cluster, we build a hierarchy of motion vectors using pairwise agglomerative clustering, and succinctly encode the hierarchy using entropy encoding. We demonstrate our approach on a client–server system that we have built for downloading time-dependent geometry. VL - 29 SN - 0097-8493 UR - http://www.sciencedirect.com/science/article/pii/S009784930500052X CP - 3 M3 - 10.1016/j.cag.2005.03.021 ER - TY - CONF T1 - Automated cluster-based Web service performance tuning T2 - 13th IEEE International Symposium on High performance Distributed Computing, 2004. Proceedings Y1 - 2004 A1 - Chung,I. -H A1 - Hollingsworth, Jeffrey K KW - Active Harmony system KW - automated performance tuning KW - business KW - cluster-based Web service system KW - Clustering algorithms KW - Computer science KW - Educational institutions KW - electronic commerce KW - Internet KW - Middleware KW - performance evaluation KW - scalability KW - Throughput KW - Transaction databases KW - Web server KW - Web services KW - workstation clusters AB - Active harmony provides a way to automate performance tuning. We apply the Active Harmony system to improve the performance of a cluster-based web service system. The performance improvement cannot easily be achieved by tuning individual components for such a system. The experimental results show that there is no single configuration for the system that performs well for all kinds of workloads. By tuning the parameters, Active Harmony helps the system adapt to different workloads and improve the performance up to 16%. For scalability, we demonstrate how to reduce the time when tuning a large system with many tunable parameters. Finally an algorithm is proposed to automatically adjust the structure of cluster-based web systems, and the system throughput is improved up to 70% using this technique. JA - 13th IEEE International Symposium on High performance Distributed Computing, 2004. Proceedings PB - IEEE SN - 0-7695-2175-4 M3 - 10.1109/HPDC.2004.1323484 ER - TY - CONF T1 - Automatic video summarization for wireless and mobile environments T2 - 2004 IEEE International Conference on Communications Y1 - 2004 A1 - Yong Rao A1 - Mundur, Padma A1 - Yesha,Y. KW - automatic video summarization KW - batch processing KW - batch processing (computers) KW - Clustering algorithms KW - Clustering methods KW - clustering scheme KW - Computer science KW - Delaunay diagram KW - graph theory KW - Gunshot detection systems KW - Image sequences KW - mesh generation KW - Mobile computing KW - mobile radio KW - multidimensional point data cluster KW - Multidimensional systems KW - Multimedia communication KW - video clip KW - video frame content KW - Video sequences KW - video signal processing KW - wireless mobile environment AB - In this paper, we propose a novel video summarization technique using which we can automatically generate high quality video summaries suitable for wireless and mobile environments. The significant contribution of this paper lies in the proposed clustering scheme. We use Delaunay diagrams to cluster multidimensional point data corresponding to the frame contents of the video. In contrast to the existing clustering techniques used for summarization, our clustering algorithm is fully automatic and well suited for batch processing. We illustrate the quality of our clustering and summarization scheme in an experiment using several video clips. JA - 2004 IEEE International Conference on Communications PB - IEEE VL - 3 SN - 0-7803-8533-0 M3 - 10.1109/ICC.2004.1312767 ER - TY - CONF T1 - Ordered treemap layouts T2 - IEEE Symposium on Information Visualization, 2001. INFOVIS 2001 Y1 - 2001 A1 - Shneiderman, Ben A1 - Wattenberg,M. KW - Clustering algorithms KW - Computer science KW - Data visualization KW - Displays KW - Electronic switching systems KW - Filling KW - Monte Carlo methods KW - Read only memory KW - Testing JA - IEEE Symposium on Information Visualization, 2001. INFOVIS 2001 PB - IEEE SN - 0-7695-7342-5 M3 - 10.1109/INFVIS.2001.963283 ER -