4 Techniques for Powerful Network Analysis

The huge amount of data being produced makes it difficult to separate what’s important from noise. Once you’ve created a graph visualization of your big data, how do you know what to look for? To focus on what’s important, we use four graph analysis and design techniques to reveal connections in the data and patterns in the structure.

“Successful networks are designed—they don’t just happen.
Knowing a network’s essential design issues—and how to make and when to change design choices—is a crucial part of the practice of building effective social-impact networks.”

– Connecting to Change the World: Harnessing the Power of Networks for Social Impact
by Peter Plastrik, Madeleine Taylor, and John Cleveland

Sometimes, layout choice leads to successful network design. Here, the circular algorithm inherently does analysis to group nodes into clusters.
Sometimes, layout choice leads to successful network design. Here, Circular layout inherently does analysis to group nodes into clusters.

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Adding Dimension with a Swimlane Matrix

Add Dimension with a Swimlane Matrix

Swimlanes organize graph visualizations in a simple, user-friendly manner. Typically, process flows use swimlane diagrams to group components into individual categories, or lanes. Each node represents a task or activity and the lanes organize them by role, function, or department. However, because swimlanes are usually defined along a single axis, they tend to be used as alternatives to other graphical cues like text, coloring, or icons. In this scenario, swimlanes only add one additional dimension of data. What if you could show more?

With the latest release of Tom Sawyer Perspectives, we’re taking our swimlane diagram capability to the next dimension.

In this Purchase Order workflow, the vertical lanes clearly show which tasks belong to which department in the company, while the horizontal lanes define the task type.
In this Purchase Order workflow, the vertical lanes show the days on which tasks need to be performed, while the horizontal lanes define the task type. This helps keep the overall process on schedule.

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Untangle the Hairball with Bundle Layout

Untangle the hairball with Bundle Layout

It’s a tale as old as time: you organize a massive database of facts and figures only to discover that the opening visualization is…a giant hairball! Sure, you can zoom in to 800% and slowly start to see connections, but the time that would take is too exhausting to consider.

Fear not! Our new Bundle Layout, now available in Tom Sawyer Perspectives 9.1.0, makes it possible to untangle, demystify, and succinctly visualize complex datasets.

The hairball visualization (left) is neatly untangled with Bundle Layout (right).
The hairball visualization (left) is neatly untangled with Bundle Layout (right).

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Using Centrality Analysis to Fight Crime

Using Centrality Analysis to Fight Crime

As the amount of data available to law enforcement agencies increases, so does the pressure for them to use that data in efficient and predictive ways to thwart crime. Centrality analysis helps agencies ask specific questions of their big data and get answers quickly.

The basic layout of upper management in a crime network.
The basic layout of upper management in a crime network.

We’ve applied our graph analytics and visualization expertise to tough problems in law enforcement, fraud detection, cybersecurity, telecommunications, and more. Of the 30+ data analysis algorithms available in Tom Sawyer Perspectives, there are four centrality algorithms that answer questions like:

  • Who has the most direct connections?
  • Who are the top-tier influencers?
  • Who are the middlemen?
  • Who is controlling the information flow?

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Coronavirus Mutations Revealed with Perspectives

Revealing Hidden Coronavirus Mutations

In the past month, the internet has exploded with information about coronavirus and the disease it causes, COVID-19. The vast amount of data feels like too much to keep up with. But that’s where graph and data visualization thrives! So, we used the power of Tom Sawyer Perspectives to visualize the continually updated genomic epidemiology data provided by Nextstrain to reveal previously unseen coronavirus mutations.

Phylogenetic data of coronavirus genomes and mutations from mid March 2020 shown in symmetric layout using Tom Sawyer Perspectives
Phylogenetic data of coronavirus genomes and mutations from mid-March 2020 shown in symmetric layout using Tom Sawyer Perspectives

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3 Quick Ways To Perfect Graph Edge Labels

Fix Inconsistent Grap Edge Lables

You gather your data, choose the best layout, and find an analysis method that gets your users the insight they need. But before you can declare victory, you need to assign meaning to the objects in your graph. Adding labels to nodes and edges might seem easy enough, but sometimes—especially with graph edge labels—their placement can cause readability issues. Since a “messy” presentation can compromise the message and meaning of your graph visualization, you’ll want to do all you can to prevent these.

Graph Edge Labels that are rotated, centered, and placed over each edge.

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