Analysis and visualization of co-authorship networks for understanding academic collaboration and knowledge domain of individual researchers

This paper proposed a new approach for collecting, analyzing and visualizing co-authoring data of individuals. This approach can be used for understanding the academic collaboration and knowledge domain of individual researchers in a past period through repetitive co-published works. Particularly we extracted the co-authoring data from the DBLP which is one of the largest on-line Computer Science bibliographic databases available on the Internet. To help users to understand the academic collaboration and knowledge domain of individuals, we developed an InterRing visualizer which shows not only the weight of co-authorship of an individual with other researchers in particular academic year, but also the knowledge domain of the individual that was covered by his/her publications published in a past period. © 2006 IEEE

Visualization of individual's knowledge by analyzing the citation networks

Visual analysis of knowledge domain is an emerging field of study as science is highly dynamic and constantly evolving. Behind the scene, a knowledge domain is formed and contributed by enormous researchers' publications that describe the common subject of the domain. There is large number of significant activities have been carried out to visualize and identify the knowledge domains of research projects, groups and communities. However, the research on visualizing the knowledge structure at individual level is relative inactive. It is difficult to track down the individual's contribution to the subject and the degree of the knowledge they possess. In this paper, we are attempting to visualize the individual's knowledge structure by analyzing the citation and co-authorship relational structures. We try to analyze and map author's documents to the knowledge domains. By mapping the documents to knowledge domain, we obtain the skeleton of knowledge structure of an individual. Then, we apply the visualization technique to present the result. © 2007 IEEE

SWING: A system for visualizing web graphs

A Web graph refers to the graph that is used to represent relationships between Web pages in cyberspace, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is a very huge graph as growing with cyberspace. This paper presents a pipeline for extracting web information from cyberspace to a web graph and layout techniques for making the web graph more readable. As the size of computer screen is limited, only a small part of the Web graph can be displayed. Several layout techniques should be adapted and combined effectively for web graph visualization. The visualization process incorporates graph drawing algorithms, layout adjustment methods, as well as filtering and clustering methods in order to decide which part of the Web graph should be displayed and how to display it based on the user's focus in navigation

Fast convergence layout algorithm for drawing graphs in marching-graph

Marching-Graph is a new visualization that integrates the graph metaphor and the spatial metaphor into a single visualization. It provides users with highly interactive maps for accessing the logical structures of information that has the geographical attributes. Instead of presenting known facts onto maps, it provides a mechanism for users to visually analyze and seek unknown knowledge through effective human-map interaction and navigation across different spaces. However, the traditional force-directed layout algorithms are very slow in reaching an equilibrium configuration of forces. They usually spend tens of seconds making the layout of a graph converge. Thus, those force-directed layout algorithms can not satisfy the requirement for drawing a sequence of graphs rapidly, while the users are quickly marching through the geographic regions. This paper proposes a fast convergence layout method that speeds up the interaction time while users are progressively exploring a sequence of graphs through a series of force-directed layouts in Marching-Graph. It essentially combines a radial tree drawing method and a force-directed graph drawing method to achieve the fast convergence of energy minimization

Highlighting in information visualization: A survey

Highlighting was the basic viewing control mechanism in computer graphics and visualization to guide users' attention in reading diagrams, images, graphs and digital texts. As the rapid growth of theory and practice in information visualization, highlighting has extended its role that acts as not only a viewing control, but also an interaction control and a graphic recommendation mechanism in knowledge visualization and visual analytics. In this work, we attempt to give a formal summarization and classification of the existing highlighting methods and techniques that can be applied in Information Visualization, Visual Analytics and Knowledge Visualization. We propose a new three-layer model of highlighting. We discuss the responsibilities of each layer in the different stage of the visual information processing. © 2010 IEEE

Exploring spatially referenced information through 2D Marching Graph

In this paper, we proposed a new visualization framework called Marching Graph that integrates the graph metaphor and the spatial metaphor into a single visualization. Marching Graph allows users to navigate the spatially referenced relational data across two different visual metaphors. We use a force-directed layout algorithm to draw a sequence of progressive graphs, G1, G 2, ... Gn in a 2D geometric space that present the spatially referenced relational data. Each graph Gi is associated with a particular geographic region Ri presented by the spatial metaphor. We allow the user to "march" through the thematic map by altering the focus region Ri and the display of its corresponding graph Gi → Ri. The use of 2D visual metaphors facilitates the navigation activities and human cognition process significantly

A new analytics model for large scale multidimensional data visualization

© Springer International Publishing Switzerland 2015. With The Rise Of Big Data, The Challenge For Modern Multidimen-Sional Data Analysis And Visualization Is How It Grows Very Quickly In Size And Complexity. In This Paper, We First Present A Classification Method Called The 5ws Dimensions Which Classifies Multidimensional Data Into The 5ws Definitions. The 5ws Dimensions Can Be Applied To Multiple Datasets Such As Text Datasets, Audio Datasets And Video Datasets. Second, We Establish A Pair-Density Model To Analyze The Data Patterns To Compare The Multidimensional Data On The 5ws Patterns. Third, We Created Two Additional Parallel Axes By Using Pair-Density For Visualization. The Attributes Has Been Shrunk To Reduce Data Over-Crowding In Pair-Density Parallel Coordinates. This Has Achieved More Than 80% Clutter Reduction Without The Loss Of Information. The Experiment Shows That Our Model Can Be Efficiently Used For Big Data Analysis And Visualization

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

TreemapBar: Visualizing additional dimensions of data in bar chart

Bar chart is a very common and simple graph that ismainly used to visualize simple x, y plots of data for numerical comparisons by partitioning the categorical data values into bars and typically limited to operate on highly aggregated dataset. In today's growing complexity of business data with multi dimensional attributes using bar chart itself is not sufficient to deal with the representation of such business dataset and it also not utilizes the screen space efficiently. Nevertheless, bar chart is still useful because of its shape create strong visual attention to users at first glance than other visualization techniques. In this article, we present a treemap bar chart + tablelens interaction technique that combines the treemap and bar chart visualizations with a tablelens based zooming technique that allows users to view the detail of a particular bar when the density of bars increases. In our approach, the capability of the original bar chart and treemaps for representing complex business data is enhanced and the utilization of display space is also optimized. © 2009 IEEE

Semantic topic discovery for lecture video

© Springer Nature Switzerland AG 2020. With more and more lecture, videos are available on the Internet, on-line learning and e-learning are getting increasing concerns because of many advantages such as high degree of interactivity. The semantic content discovery for lecture video is a key problem. In this paper, we propose a Multi-modal LDA model, which discovers the semantic topics of lecture videos by considering audio and visual information. Specifically, the speaking content and the information of presentation slides are extracted from the lecture videos. With the proposed inference and learning algorithm, the semantic topics of the video can be discovered. The experimental results show that the proposed method can effectively discover the meaningful semantic characters of the lecture videos