A Framework for Visualising Large Graphs
Seok-hee Hong2005, Ninth International Conference on Information Visualisation (IV'05)
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
Visualising large graphs faces the challenges of both data complexity and visual complexity. This paper presents a framework for visualising large graphs that reduces data complexity using the clustered graph model and provides users with navigational approaches for browsing clustered graphs. A key design task of such a system is to define a strategy for generating logical abstractions of a clustered graph during navigation. An appropriate abstraction strategy should represent a clustered graph well and avoid visual overload. The semantic fisheye view of a clustered graph is proposed for such a purpose. Two case studies were investigated, and the experiment results show that during navigation the first-order fisheye view of a clustered graph conserves visual complexity at a constant level.
Related papers
Automatic generation of interactive overview diagrams for the navigation of large graphs
Information and Computation/information and Control, 2000
A clustered graph can be used to build an abstract view of its non-clustered counterpart and reduce visual complexity. The classic approach to interaction with a clustered graph is limited in scalability and efficacy, underlining the need for an overview diagram. We present a technique for the automatic generation of an overview diagram based on hierarchical clustering and discuss its application to graphs. Hierarchical clustering induces a tree structure that is useful as a map to navigate the original data set. Because the resulting overview diagram is itself a graph, it can be manipulated by the same tools that are available for graphs.
Efficient visual navigation of hierarchically structured graphs
2006
Visual navigation of hierarchically structured graphs is a technique for interactively exploring large graphs that possess an additional hierarchical structure. This structure is expressed in form of a recursive clustering of the nodes: in call graphs of telephone networks, for instance, the nodes are identified with phone numbers; they are clustered recursively through the implicit structure of the numbers, e. g., nodes with the same area code belong to a cluster. In order to reduce the complexity and the size of the graph, only those subgraphs that are currently needed are shown in detail, while the others are collapsed, i. e., represented by meta nodes. In such a graph view the subgraphs in the areas of interest are expanded furthest, whereas those on the periphery are abstracted. As the areas of interest change over time, clusters in a view need to be expanded or contracted. First and foremost, there is need for an efficient data structure for this graph view maintenance problem...
GMine: interactive browsing of large graphs
IV Workshop on Information Visualization and Analysis in Social Networks - Brazilian Symposium on Databases, 2008
Graphs are abstract representations that can describe a large set of real world phenomena and that, possibly, scale to the order of hundreds of thousands of nodes and millions of edges. Benefiting from such graphs can be better performed by means of visual interaction. However, in the domain of large graphs, excessive processing and limited display space bound the possibilities for visual presentation and processing. In this line, we introduce GMine, a prototype system that uses an innovative data structure, the Graph-Tree. The engineering of GMine allows for scalability over huge graphs stored on disk, an extended graph representation embracing both hierarchical and plain organization, and the interactive browsing of graph hierarchies.
Visualizing Large Graphs with Compound-Fisheye Views and Treemaps
Lecture Notes in Computer Science, 2005
Compound-fisheye views are introduced as a method for the display and interaction with large graphs. The method relies on a hierarchical clustering of the graph, and a generalization of the traditional fisheye view, together with a treemap representation of the cluster tree.
Graph Visualization and Navigation in Information Visualization:A Survey
IEEE Transactions on Visualization and Computer Graphics, 2000
This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, state-transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective.
Visual analysis of large graphs : state-of-the-art and future research challenges
Computer Graphics Forum, 2011
The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.
Clustering graphs for visualization via node similarities
Journal of Visual Languages & Computing, 2006
Graph visualization is commonly used to visually model relations in many areas. Examples include Web sites, CASE tools, and knowledge representation. When the amount of information in these graphs becomes too large, users, however, cannot perceive all elements at the same time. A clustered graph can greatly reduce visual complexity by temporarily replacing a set of nodes in clusters with abstract nodes. This paper proposes a new approach to clustering graphs. The approach constructs the node similarity matrix of a graph that is derived from a novel metric of node similarity. The linkage pattern of the graph is thus encoded into the similarity matrix, and then one obtains the hierarchical abstraction of densely linked subgraphs by applying the k-means algorithm to the matrix. A heuristic method is developed to overcome the inherent drawbacks of the k-means algorithm. For clustered graphs we present a multilevel multi-window approach to hierarchically drawing them in different abstract level views with the purpose of improving their readability. The proposed approaches demonstrate good results in our experiments. As application examples, visualization of part of Java class diagrams and Web graphs are provided. We also conducted usability experiments on our algorithm and approach. The results have shown that the hierarchically clustered graph used in our system can improve user performance for certain types of tasks. r relegating nodes to the background.
Methods for Software Visualization of Large Graph Data Structures
International Journal on Advanced Science, Engineering and Information Technology
In this paper, three different methods for software visualization of large graph structures, respectively Rectangle, Intersection and Combined are presented. The basic concepts for using software development environments are outlined. Their capabilities for visual designing and event-oriented programming are discussed. A brief analysis of the basic features of the environment used to develop the ClipRect Monitor application is made. The main functions of this software are also presented. All experimental results in this study are generated with this application. According to the methodology, six graphs are prepared to determine the effectiveness of the three methods. The number of vertices and the edges of these graphs are proportional to the size of the drawing area (canvas). The drawing areas are also six and have different sizes, such that each subsequent area has a height and width twice the size of the previous one. Besides, for all areas, the width/height ratio is exactly 16:9. This ratio is widely used in monitors as well as laptops, mobile phones and tablets. The largest drawing area that the ClipRect Monitor application scanned during the experiments is 128 000 x 72 000 pixels. This scan is performed for graph G_6 with 1 415 vertices and 100 000 edges. The visualization area is diagonally positioned relative to the drawing area. For each visualization area, each of the three methods, respectively Rectangle, Intersection and Combined is performed. The Combined method executes the Rectangle method first and then the Intersection method. The results show that the Intersection method was the slowest compared to the other two methods in terms of the number of edges of the graph that are analyzed. When the visualization area is internal to the drawing area, the Rectangle method performs better than the Combined method. The Rectangle method gives the best result in terms of time for analysis and drawing of the edges of the graph. The Combined method combines the characteristics of the other two methods. This method is optimal in terms of the time of analysis of the need to draw the edges of the graph relative to the number of drawn edges.
Graph structures for data visualizations
Serbian Journal of Engineering Management, 2021
Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be n...
MGV: a system for visualizing massive multidigraphs
IEEE Transactions on Visualization and Computer Graphics, 2002
We describe MGV, an integrated visualization and exploration system for massive multi-digraph navigation. It adheres to the Visual Information-Seeking Mantra: Overview first, zoom and filter, then details on demand. MGV's only assumption is that the vertex set of the underlying digraph corresponds to the set of leaves of a predetermined tree ¢ . MGV builds an out-of-core graph hierarchy and provides mechanisms to plug in arbitrary visual representations for each graph hierarchy slice. Navigation from one level to another of the hierarchy corresponds to the implementation of a drill-down interface. In order to provide the user with navigation control and interactive response, MGV incorporates a number of visualization techniques like interactive pixel-oriented 2D and 3D maps, statistical displays, color maps, multi-linked views, and a zoomable label based interface. This makes the association of geographic information and graph data very natural. To automate the creation of the vertex set hierarchy for MGV, we use the notion of graph sketches. They can be thought of as visual indices that guide the navigation of a multi-graph too large to fit on the available display. MGV follows the client-server paradigm and it is implemented in C and Java-3D. We highlight the main algorithmic and visualization techniques behind the tools and point out along the way several possible application scenarios. Our techniques are being applied to multi-graphs defined on vertex sets with sizes ranging from 100 million to 250 million vertices 1 .
Related papers
A multi-graph approach to complexity management in interactive graph visualization
Computers & Graphics, 2006
In this paper we describe a new, multi-graph approach for development of a comprehensive set of complexity management techniques for interactive graph visualization tools. This framework facilitates efficient implementation of management of multiple associated graphs with navigation links and nesting of graphs as well as ghosting, folding and hiding of unwanted graph elements. The theoretical analyses show that the involved data structures and operations on them are quite efficient, and an implementation in a graph drawing tool has proven to be successful.
Reduction of visual complexity in dynamic graphs
Graph Drawing, 1995
Graphs are used extensively in software visualization to represent both static aspects of software structure and dynamic aspects of execution-time behavior. However, for realistic subject software systems, there are far too many nodes and edges in the displayed graphs to be comprehensible to an end user. Further, for presentation of dynamics, continual change and redisplay of such large graphs is too demanding for conventional workstation computational resources. This paper poses the problem of "reduction" or "abstraction" in dynamically changing graphs, and proposes a combination of techniques that can be used to reduce the visual complexity of a graph, without obscuring the significant information that it was meant to convey. The abstract graph can be comprehended more readily and it changes far less frequently than the full graph. As well, when the abstract graph does change, it requires far less computation for layout and redisplay. These abstraction techniques are illustrated by way of examples showing their use in systems for visualization of object-oriented and multi-layer software systems.
Large Graph Visualization by Hierarchical Clustering
Journal of Software, 2008
This paper proposes a new technique for visualizing large graphs of several ten thousands of vertices and edges. To achieve a graph abstraction, a hierarchical clustered graph is extracted from a general large graph based on the community structures discovered in the graph. An enclosure geometrical partitioning algorithm is then applied to achieving the space optimization. For graph drawing, it uses a combination of spring-embbeder and circular drawing algorithms that archives the goal of optimization of display space and aesthetical niceness. The paper also discusses an interaction mechanism accompanied with the layout solution. The interaction not only allows users to navigate hierarchically through the entire clustered graph, but also provides a way to navigate multiple clusters concurrently. Animation is also implemented to preserve user mental maps during the interaction.
Visualization and analysis of large graphs
Proceedings of the ACM first Ph. …, 2007
In Knowledge engineering, synthesized information has often an evolving and relational form. Information representation using graphs may ease data interpretation for non-expert users. However this graph may be complex and simplifications are useful in order to ease analysis. In this article, we present VisuGraph, a powerful tool for graph drawing. This tool gives the possibility to reduce large graph by two techniques: the Markov CLustering algorithm (MCL) application and the global graph division in time-sliced visualizations in order to specify and to simplify temporal analysis.
An object-oriented design for graph visualization
Software - Practice and Experience, 2001
Many applications, from everyday file system browsers to visual programming tools, require the display of network and graph structures. The Graph Visualization Framework 2 (GVF) is an architecture that supports the tasks common to most graph browsers and editors. This article gives a brief overview of the design of the GVF and focuses on the core classes that are used to represent and manipulate graphs. The design of the core classes is justified by the requirements for navigation and visualization.
Visual Analysis of Large Graphs Using (X,Y)-Clustering and Hybrid Visualizations
IEEE Transactions on Visualization and Computer Graphics, 2011
Many different approaches have been proposed for the challenging problem of visually analyzing large networks. Clustering is one of the most promising. In this paper we propose a new clustering technique whose goal is that of producing both intra-cluster graphs and inter-cluster graph with desired topological properties. We formalize this concept in the (X,Y)-clustering framework, where Y is the class that defines the desired topological properties of intracluster graphs and X is the class that defines the desired topological properties of the inter-cluster graph. By exploiting this approach hybrid visualization tools can effectively combine different node-link and matrix-based representations, allowing users to interactively explore the graph by expansion/contraction of clusters without loosing their mental map. As a proof of concept, we describe the system VHYXY (Visual Hybrid (X,Y)-clustering) that implements our approach and we present the results of case studies to the visual analysis of social networks.
Fisheye Tree Views and Lenses for Graph Visualization
Tenth International Conference on Information Visualisation (IV'06), 2006
We present interactive visual aids to support the exploration and navigation of graph layouts. They include Fisheye Tree Views and Composite Lenses. These views provide, in an integrated manner, overview+detail and focus+context. Fisheye Tree Views are novel applications of the well known fisheye distortion technique. They facilitate the exploration of the hierarchy trees associated with clustered graphs. Composite Lenses are the result of the integration of several lens techniques. They facilitate the display of local graph information that may be otherwise difficult to grasp in large and dense graph layouts.
Clustering, visualizing, and navigating for large dynamic graphs
2013
Abstract. In this paper, we present a new approach to exploring dy-namic graphs. We first propose a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to de-fine two visual representations. The first view is an overview that shows how clusters evolve over time and provides an interface to find and select interesting time-steps. The second view consists of a node link diagram of a selected time-step which uses the clustering to efficiently define the layout. By using the time-dependant clustering, we ensure the stability of our visualization and preserve user mental map by minimizing node motion, while simultaneously producing an ideal layout for each time step. Also, as the clustering is computed ahead of time, the second view updates in linear time which allows for interactivity even for graphs with upwards of tens of thousands of nodes. 1
Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs
IEEE Access
Nowadays there is a great interest in the visualization of property graphs to make their navigation, inspection, and visual analysis easier. However, property graphs can be quite large and their rendering on web browsers can lead to a dark cloud of points that is difficult to visually explore. With the aim of reducing the size of the visualized graph, several approaches have been proposed for substituting clusters of related vertices with aggregated meta-nodes and introducing meta-edges among them, but they usually consider the graph in main-memory and do not adopt efficient data structures for extracting parts of it from the disk. The purpose of this paper is to optimize the preparation of the graph to be visualized according to a certain resolution level by introducing refined data structures and specifically tailored algorithms. By means of them, the rendering time is reduced when changing the current visualization through zoom-in, zoomout, and related operations. Starting from a cluster hierarchy that represents the possible aggregations of graph nodes, in the paper we characterize a visualization according to a horizontal slice of the hierarchy and propose indexing structures and incremental algorithms for quickly passing to a new visualization with minimal changes of the current one. In this process, we ensure a consistent and efficient aggregation of addictive properties associated with nodes and edges. An extensive experimental analysis has been conducted to assess the quality of the proposed solution. INDEX TERMS Property graphs, node indices, edge indices, aggregations according to a cluster hierarchy, multi-resolution visualization, zoom-in and zoom-out operations, incremental algorithms.
Navigation and Interaction in Graph Visualizations
Revista de Informática Teórica e Aplicada, 2008
Graphs are widely utilized in many fields and several applications require their visualization. Graph visualization is based on techniques for graph drawing, interaction and navigation in such a way that helps the user in finding and manipulating information efficiently. These techniques, which can be two or three-dimensional, depending on the spatial metaphor used to represent the graph, can be combined in many different ways in order to fit a particular application's needs. This paper presents an overview of the field of graph visualization.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.