CET: a tool for creative exploration of graphs (original) (raw)

On the integration of graph exploration and data analysis: The creative exploration toolkit

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012

To enable discovery in large, heterogenious information networks a tool is needed that allows exploration in changing graph structures and integrates advanced graph mining methods in an interactive visualization framework. We present the Creative Exploration Toolkit (CET), which consists of a state-of-the-art user interface for graph visualization designed towards explorative tasks and support tools for integration and communication with external data sources and mining tools, especially the data-mining platform KNIME. All parts of the interface can be customized to fit the requirements of special tasks, including the use of node type dependent icons, highlighting of nodes and clusters. Through an evaluation we have shown the applicability of CET for structure-based analysis tasks.

PIWI: Visually Exploring Graphs Based on Their Community Structure

IEEE Transactions on Visualization and Computer Graphics, 2000

Community structure is an important characteristic of many realnetworks, which shows high concentrations of edges within special groupsof vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships amongcommunities, identifying attribute-structure relationships, andselecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks.The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated,uncluttered visualizations and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.

Gmine: a system for scalable, interactive graph visualization and mining

2006

Abstract Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow.

Semantic Blossom Graph: A New Approach for Visual Graph Exploration

2014 18th International Conference on Information Visualisation, 2014

Graphs are widely used to represent relationships between entities. Indeed, their simplicity in depicting connectedness backed by a mathematical formalism, make graphs an ideal metaphor to convey relatedness between entities irrespective of the domain. However, graphs pose several challenges for visual analysis. A large number of entities or a densely connected set quickly render the graph unreadable due to clutter. Typed relationships leading to multigraphs cannot clearly be represented in hierarchical layout or edge bundling, common clutter reduction techniques. We propose a novel approach to visual analysis of complex graphs based on two metaphors: semantic blossom and selective expansion. Instead of showing the whole graph, we display only a small representative subset of nodes, each with a compressed summary of relations in a semantic blossom. Users apply selective expansion to traverse the graph and discover the subset of interest. A preliminary evaluation showed that our approach is intuitive and useful for graph exploration and provided insightful ideas for future improvements.

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.

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.

BGS: A Large-Scale Graph Visualization Tool

Electronic Imaging, 2018

We present BGS (Big Graph Surfer), a scalable graph visualization tool that creates hierarchical structure from original graphs and provide interactive navigation along the hierarchy by expanding or collapsing clusters when visualizing large-scale graphs. A distributed computing framework-Spark provides the backend for BGS on clustering and visualization. This architecture makes it capable of visualizing a graph bigger than 1 billion nodes or edges in real-time after preprocessing. In addition, BGS provides a series of hierarchy and graph exploration methods, such as hierarchy view, hierarchy navigation, hierarchy search, graph view, graph navigation, graph search, and other useful interactions. These functionalities facilitate the exploration of very large-scale graphs. To evaluate the effectiveness of BGS, we apply BGS to several large-scale graph datasets, and discuss its scalability, usability, and flexibility.

The state of the art in visualizing dynamic graphs

Figure 1: Illustrated hierarchical taxonomy of dynamic graph visualization techniques; the number of published techniques per taxonomic category is encoded in the brightness of the background (for details see Table 4).

Carbonic: A Framework for Creating and Visualizing Complex Compound Graphs

Applied Sciences

Advances in data generation and acquisition have resulted in a volume of available data of such magnitude that our ability to interpret and extract valuable knowledge from them has been surpassed. Our capacity to analyze data is hampered not only by their amount or their dimensionality, but also by their relationships and by the complexity of the systems they model. Compound graphs allow us to represent the existing relationships between nodes that are themselves hierarchically structured, so they are a natural substrate to support multiscale analysis of complex graphs. This paper presents Carbonic, a framework for interactive multiscale visual exploration and editing of compound graphs that incorporates several strategies for complexity management. It combines the representation of graphs at multiple levels of abstraction, with techniques for reducing the number of visible elements and for reducing visual cluttering. This results in a tool that allows both the exploration of existi...

CGV—An interactive graph visualization system

2009

Previous work on graph visualization has yielded a wealth of efficient graph analysis algorithms and expressive visual mappings. To support the visual exploration of graph structures, a high degree of interactivity is required as well.