Visualization of large citation networks with space-efficient multi-layer optimization

This paper describes a technique for visualizing large citation networks (or bibliography networks) using a space-efficient multi-layer optimization visualization, technique. Our technique first use a fast clustering algorithm to discover community structure in the bibliographic networks. The clustering process partitions an entire network into relevant abstract subgroups so that the visualization, can provide a clearer and less density of display of global view of the complete graph of citations. We next use a new space-efficient visualization algorithm to archive the optimization of graph layout within the limited display space so that our technique can theoretically handle a very large bibliography network with several thousands of elements. Our technique also employs rich graphics to enhance the attributed property of the visualization including publication years and number of citations. Finally, the system provides an interaction technique in cooperating with the layout to allow users to navigate through the citation network. Animation is also implemented to preserve the users' mental maps during the interaction

Large scale network analysis with interactive visualisation

This paper proposes a new interactive visualisation for analysing large hierarchical structures and networks. The technique combines of different graph layout methods with a layout refinement process, an interactive navigation mechanism and clustering algorithms. The integration of these components makes it flexible in dealing with a variety of graph and hierarchical structures. Interactive exploration is enabled with chaincontext view. We aim to provide user with an effective mechanism for understanding of the nature of various networks. This could lead to the discovering and revealing of the hidden structures and relationships among elements as well as relationships associated with the elements. © 2009 IEEE

A usability study on the use of multi-context visualization

Graph visualization has been widely used in real-world applications, as it provides better presentation of overall data structure. However, there are navigation problems existing in deep and large relational datasets. To address these challenges, a new technique called multi-context visualization, which provides users with rich contextual information, has been proposed as the solution to the navigation in large scale datasets. This paper evaluates the multi-context visualization by conducting an experiment-based user study. To answer whether the more contextual information positively assist in making more accurate and easier decisions, it aims to evaluate the effectiveness and efficiency of the multi-context visualization, by measuring the user performance. Specifically, this usability test was designed to test if the use of multiple context views can improve navigation problems for deep and large relational data sets. © 2008 IEEE

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

Interactive visualization with user perspective: A new concept

With an astonishing amount of data generated for processing on a daily basic, it is essential to provide an effective methodology for understanding, reasoning and supporting decision making of large information spaces. This paper presents a new concept that provides an intelligent and interactive visualization in supporting large scale analysis. This aims to provide a much greater flexibility and control for the users to interactively customize the visualizations according to their preferences. A simple prototype is also presented to demonstrate the concept on hierarchical structures. Copyright © 2010 ACM

A visualization approach for frauds detection in financial market

The traditional solutions to the stock market security are not sufficient in identifying attackers and further attack plans from the analysis of existing events. Therefore, it is difficult for analysts to prevent future unexpected events or frauds by only monitoring the realtime trading information. The event-driven fraud detection in financial market could not help analysts to find attack plans and the further intention of attackers. This paper proposed a new framework of visual analytics for stock market security. The proposed solution consists of two stages: 1) Visual Surveillance of Market Performance, and 2) Behavior-Driven Visual Analysis of Trading Networks. In the first stage, we use a 3D treemap to monitor the realtime stock market performance and to identify a particular stock that produced an unusual trading pattern. We then move to the next stage: social network visualization to conduct behavior-driven visual analysis of suspected pattern. Through the visual analysis of social (or trading) network, analysts may finally identify the attackers (the sources of the fraud), and further attack plans. © 2009 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. © 2011 IEEE

A visual method for high-dimensional data cluster exploration

Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration. © 2009 Springer-Verlag Berlin Heidelberg

A technique for visualizing dihedral signal of large protein sequences

This paper presents a clustering and visualization technique for analyzing dihedral angles of large protein sequences. The clustering is used for discovering and grouping those similar dihedrals while the visualization can display and navigate sequences of dihedral angles of several proteins as well as their clustered property. In order to visualize a very large sequence of hundred thousands of dihedral signals, we plot them on a spiral coordinate system. This spiral visualization ensures the linear distribution without distortion or interruption of a very long sequence of points. A clustering algorithm is also provided to group those dihedral signals into different clusters so that it can enhance the analysis process. Our system can also zoom to display a number of selected proteins interactively. © 2006 IEEE

A zoomable shopping browser using a graphic-treemap

Effective and efficient navigation and representation of the entire structure of the product catalogue is one of the important factors for on-line market. This paper proposes an application using Treemaps visualization to enhance the functionality of online product category. We aim to develop high-quality catalog interfaces in terms of readability, understandability and comprehension by integrating graphics into Treemaps. We applied two types of Treemaps: 1) Slice-and-Dice Treemap, 2) Squarified Treemap, into the on-line catalogue to address the small windowproblem allowing buyers to overview and navigate large product categories dynamically. We also use a history bar that locates on the top of each category and sub-category to provide a 2.5-dimensional view of contextual information. © 2009 IEEE