About this Talk
This master class provides best practices for visual analytics of graph data across various tools. You’ll learn how to better use data visualization to enhance cognitive abilities and accelerate the human process of solving complex problems.
Many visualization techniques have been developed for tabular data, but graph data demands more sophisticated approaches to enable meaningful insights. Using real-world examples and various graph technologies, we will walk through:
- Visualization of Graph Data: Techniques for representing graph data, including transformations to high-dimensional, associative, and hierarchical structures.
- Visual Strategy: Effective use of color, shape, size, distribution, animation, and shifting perspectives to convey information.
- Visual Transformation Strategy: Visual methods for performing graph-based calculations and data transformations.
- Visual Analytics Workflow: Best practices for creating repeatable, traceable workflows in visual analytics.
Using a laptop, you will be able to follow along a set of hands-on exercises using tools like Gephi, Neo4j Bloom, Jupyter Notebook, and GraphXR. You’ll walk away with an understanding of when to use infographics, typically designed for communication, to present information in a highly simplified, focused manner for specific topics. And we’ll contrast that with when to use visual analytics for discovery tasks that emphasizes an exploratory process aimed at uncovering insights from messy and complex data.
Join us for practical tips on communicating your graph data with an approach that emphasizes analytical reasoning through interactive visualizations and intuitive visual interfaces.
Key Topics
- Visualisation of Graph Data: Techniques for representing graph data, including transformations to high-dimensional, associative, and hierarchical structures.
- Visual Strategy: Effective use of colour, shape, size, distribution, animation, and shifting perspectives to convey information.
- Visual Transformation Strategy: Visual methods for performing graph-based calculations and data transformations.
- Visual Analytics Workflow: Best practices for creating repeatable, traceable workflows in visual analytics.
Target Audience
- Data Analysts
- Data Scientists
- Data Engineering
- Machine Learning Engineers
Goals
Gain hands-on experience in applying graph based visual analytics to solve complex data problems. Learn graph data visualisation techniques, and best practices in visual analytics.
Session outline:
- Graph Data Visualization with hands on exercises:
- Overview of key visualisation techniques.
- Exploration of visual elements for effective information display.
- Overview of common tools: Gephi, Bloom, Python libraries, JavaScript libraries, GraphXR.
- Advanced Visualization Techniques with hands on exercises:
- Shifting perspectives and dynamic visualisations.
- Interconnection between graph connection and high-dimensionality.
- Introduction to Visual Analytics:
- How is visual analytics different from visualisation?
- Visual methods for graph-based calculations and data transformations.
- Schema, and the evolution of schema during an analytics process.
- Practical exercises in conducting visual analytics.
- Introduction to Visual Analytics Workflows.
- Best practices for creating repeatable and traceable workflows in visual analytics.
Format
This class is highly hands-on. We’ll start with a brief lecture, but will quickly transition to practical data visualisation exercises. We’ll be using tools like Gephi, Bloom, Jupyter Notebook, and GraphXR to work through a series of exercises, from simple to complex. We’ll learn both the commonalities and the key differences between these tools.
Graph data transformations will be demonstrated using Cypher and visual transforms in GraphXR. Exercises will be conducted in Neo4j Desktop, Bloom, and GraphXR. The introduction to visual analytics workflows will be delivered in a lecture format.
Level
Intermediate
Prerequisite Knowledge
Basic knowledge of Python and Cypher will be helpful for the hands-on exercises, but much of the lecture will cover general visualisation approaches that are broadly applicable.
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