Drawing large weighted graphs using clustered force-directed algorithm

© 2014 IEEE. Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights

Visual clustering of spam emails for DDoS analysis

Networking attacks embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The use of these intrusion detection systems has given rise to other two issues, 1) the presentation and understanding of large amounts of spam emails, 2) the user-assisted input and quantified adjustment during the analysis process. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight'for the analysis of spam email viruses and attacks. We then use a visual clustering method to classify and display the spam emails. The visualization allows users to interactively select and scale down the scope of views for better understanding of different types of the spam email attacks. The experiment shows that this new model with the clustering visualization can be effectively used for network security analysis. © 2011 IEEE

Visual Clustering of Spam Emails for DDoS Analysis

Networking attacks embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The use of these intrusion detection systems has given rise to other two issues, 1) the presentation and understanding of large amounts of spam emails, 2) the user-assisted input and quantified adjustment during the analysis process. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight'for the analysis of spam email viruses and attacks. We then use a visual clustering method to classify and display the spam emails. The visualization allows users to interactively select and scale down the scope of views for better understanding of different types of the spam email attacks. The experiment shows that this new model with the clustering visualization can be effectively used for network security analysis

Recommender Systems for Scientific and Technical Information Providers

Providers of scientific and technical information are a promising application area of recommender systems due to high search costs for their goods and the general problem of assessing the quality of information products. Nevertheless, the usage of recommendation services in this market is still in its infancy. This book presents economical concepts, statistical methods and algorithms, technical architectures, as well as experiences from case studies on how recommender systems can be integrated

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions