Semantic Blossom Graph: A New Approach for Visual Graph Exploration (original) (raw)

Treeplus: Interactive exploration of networks with enhanced tree layouts

2006

Despite extensive research, it is still difficult to produce effective interactive layouts for large graphs. Dense layout and occlusion make food webs, ontologies, and social networks difficult to understand and interact with. We propose a new interactive Visual Analytics component called TreePlus that is based on a tree-style layout. TreePlus reveals the missing graph structure with visualization and interaction while maintaining good readability. To support exploration of the local structure of the graph and gathering of information from the extensive reading of labels, we use a guiding metaphor of "Plant a seed and watch it grow." It allows users to start with a node and expand the graph as needed, which complements the classic overview techniques than can be effective at -but often limited to -revealing clusters. We describe our design goals, describe the interface, and report on a controlled user study with 28 participants comparing TreePlus with a traditional graph interface for six tasks. In general, the advantage of TreePlus over the traditional interface increased as the density of the displayed data increased. Participants also reported higher levels of confidence in their answers with TreePlus and most of them preferred TreePlus.

Visualizing Graphs as Trees: Plant a seed and watch it grow

2006

TreePlus is a graph browsing technique based on a tree-style layout. It shows the missing graph structure using interaction techniques and enables users to start with a specific node and incrementally explore the local structure of graphs. We believe that it supports particularly well tasks that require rapid reading of labels.

A Framework for Visualising Large Graphs

Ninth International Conference on Information Visualisation (IV'05), 2005

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.

Graph-based Relational Data Visualization

Relational databases are rigid-structured data sources characterized by complex relationships among a set of relations (tables). Making sense of such relationships is a challenging problem because users must consider multiple relations, understand their ensemble of integrity constraints, interpret dozens of attributes, and draw complex SQL queries for each desired data exploration. In this scenario, we introduce a twofold methodology; we use a hierarchical graph representation to efficiently model the database relationships and, on top of it, we designed a visualization technique for rapidly relational exploration. Our results demonstrate that the exploration of databases is deeply simplified as the user is able to visually browse the data with little or no knowledge about its structure, dismissing the need for complex SQL queries. We believe our findings will bring a novel paradigm in what concerns relational data comprehension.

CET: a tool for creative exploration of graphs

2010

We present a tool for interactive exploration of graphs that integrates advanced graph mining methods in an interactive visualization framework. The tool enables efficient exploration and analysis of complex graph structures. For flexible integration of state-of-the-art graph mining methods, the viewer makes use of the open source data mining platform KNIME. In contrast to existing graph visualization interfaces, all parts of the interface can be dynamically changed to specific visualization requirements, including the use of node type dependent icons, methods for a marking if nodes or edges and highlighting and a fluent graph that allows for iterative growing, shrinking and abstraction of (sub)graphs.

NicheWorks—Interactive Visualization of Very Large Graphs

Journal of Computational and Graphical Statistics, 1999

The difference between displaying networks with 100-1000 nodes and displaying ones with 10,000-100,000 nodes is not merely quantitative, it is qualitative. Layout algorithms suitable for the former are too slow for the latter, requiring new algorithms or modified (often relaxed) versions of existing algorithms to be invented. The density of nodes and edges displayed per inch of screen real estate requires special visual techniques to filter the graphs and focus attention. A system for investigating and exploring such large, complex data sets needs to be able to display both graph structure and node and edge attributes so that patterns and information hidden in the data can be seen. We describe a tool that addresses these needs, the NicheWorks tool. We describe and comment on the available layout algorithms and the linked views system, and detail an examPle of the use of NicheWorks for analyzing web sites.

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.

Interactive Visualization of Small World Graphs

Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using a recently developed force model. We use a cross referenced database of 500 artists as a running example.

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.

VizTract: Visualization of Complex Social Networks for Easy User Perception

Big Data and Cognitive Computing

Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a v...