How to Create Network Graph Visualizations in Microsoft PowerBI

Microsoft PowerBI is a probably the most fashionable Business Intelligence (BI) instruments, and whereas it has all of the options it’s worthwhile to create dynamic analytic reporting for stakeholders throughout the enterprise, creating some superior knowledge visualizations is more difficult.

This text will stroll via methods to create giant community graph visualizations in Microsoft PowerBI to allow dynamic and interactive exploration of interconnected datasets corresponding to provide chain networks, monetary transactions, and way more.

However earlier than we do this, let’s check out some fast foundations of community graphs. 

Community Graph Foundations

Knowledge for community graphs, known as “graph knowledge” is knowledge formatted in node and edge format. Nodes symbolize discrete issues and edges symbolize the relationships between nodes.

Let’s take a easy instance of an internet social community, which will be represented in graph format. 

Nodes check with profiles, whereas edges check with following standing. 

A easy community of three profiles may find yourself trying like this:

When visualizing community graphs, we will embed extra details about nodes and edges in varied methods, corresponding to however not restricted to:

1. Node dimension
2. Edge dimension
3. Node colour
4. Edge colour
5. Labels

Structuring Community Knowledge

So now that you recognize the essential constructing blocks of a community graph, how do you construction and remodel your dataset?

Graph Knowledge is In all places

When you could be pondering, “we solely have relational knowledge the place I’m at”, that’s typically not the case. In reality, a number of relational datasets will be visualized as a community graph.

Let’s take a easy gross sales desk for example with columns for product title, buyer title, and amount. 

We are able to symbolize this similar gross sales desk as a community graph by representing each product title because the node kind “product”, buyer title because the node kind “buyer”, and every row as the sting “Bought”.

Visualized as a community graph, this may look one thing like:

Graph Knowledge Codecs

There are just a few methods this knowledge is structured, corresponding to however not restricted to:

1. Node & Edge Lists (Typically in .csv format)
2. Graph Databases (Akin to Neo4j)
3. Graph Information (corresponding to GraphML or GEXF)

However on this article, we might be utilizing a mixed node and edge record right into a single tabular dataset as a result of necessities of creating community graphs inside Microsoft PowerBI. 

Mapping Your Knowledge

You’ll have to map your knowledge to the next tabular format with every file representing an edge:

1. Supply Node (Required) -> It is a distinctive identifier of the beginning node of the sting (for instance, Buyer ID)
2. Goal Node (Required) -> It is a distinctive identifier of the ending node of the sting (for instance, Product ID)
3. Supply Coloration -> It is a class identifier for the supply node (for instance, Buyer Kind)
4. Goal Coloration -> It is a class identifier for the goal node (for instance, Product Class)
5. Hyperlink Coloration -> It is a class identifier for the sting (for instance, Gross sales Channel)

Creating the Community Graph Visualization

Now that now we have our knowledge mapped, we will create the community graph visualization. 

Whereas Microsoft doesn’t embody a community visible within the default PowerBI visuals, we will entry the visible market to obtain third-party visuals.

For this text, we might be utilizing the visible “Astra”, which helps you to create large-scale community graphs with loads of customization choices.

After you have it put in, it is going to be in your visible library.

Drag the visible onto your canvas, choose it, and observe the values required (which we mapped earlier). The visible additionally has choices to go x and y coordinates in addition to customized labels, nevertheless we received’t use these choices on this article.

The one required values are “Supply Node” and “Goal Node” so let’s begin there. Drag the columns you mapped to these nodes from the information pane.

You’ll discover the visible graphs our nodes and edges, nevertheless, it isn’t trying so nice. We’ll want to vary among the simulation settings.

To alter the simulation settings, open the formatting pane, then simulation, and improve each the hyperlink distance and repulsion pressure. I selected to set repulsion to 0.3, and hyperlink distance to fifteen.

Now you can see that we get a a lot better format of our knowledge.

Let’s now encode some extra info into the graph, by altering the node colour based mostly on node classes. Drag the fields you mapped above to Supply Coloration and Goal Coloration.

You’ll now discover the nodes are coloured in a different way and now we have a legend on the visible. 

 Let’s do some formatting to the background colour and node colours within the formatting pane.

Congratulations! You’ve created a community graph visualization in PowerBI with dynamic node coloring. 

We add much more info to the graph, for instance:

• Activate node weight to make nodes with extra edges bigger in dimension
• Including a hyperlink class to the colour the hyperlinks
• Including totally different labels to the nodes

However we aren’t executed there.

As soon as now we have the visualization, stakeholders have to make use of it to make extra knowledgeable selections.

Interacting with the Community Graph

There may be rapid worth in a static community graph, corresponding to with the ability to visually see how knowledge is interconnected via relationships. 

Nonetheless, there are some extra options we will use to make the visualization extra insightful. 

First, we will work together with the legend by deciding on classes to focus on them on the graph. For instance, rapidly finding Widgets within the graph:

We are able to additionally choose particular person nodes within the graph by clicking on them.

Alternatively, you’ll be able to toggle “choose adjoining nodes” within the node properties to have it choose not simply the node clicked on, however all nodes immediately linked to it via an edge.

For instance, deciding on “Widget A” with “choose adjoining nodes” on exhibits all clients who’ve bought that widget:

However deciding on nodes doesn’t simply spotlight them within the visualization, it passes that filter to the remainder of your PowerBI report.

This implies we will add extra charts to offer some extra context to the consumer’s picks. 

For instance, including a bar chart for amount bought by buyer:

We are able to additionally do the reverse by filtering the information going into the community visible. This may be achieved in a number of methods, corresponding to:

1. Slicers
2. Choosing items of different charts, corresponding to a slice of a donut chart
3. Filter pane

Let’s use a slicer to slice the graph on Buyer Kind:

Constructing Advanced BI Studies

Whereas the instance community graph on this article is comparatively easy for demonstration functions, you’ll be able to construct fairly advanced BI reporting for stakeholders. 

The Astra PowerBI visible used on this article can scale to tons of of hundreds of edges, and paired with extra cross-filtered visuals & slicers can allow extra superior analytics than is feasible with default PowerBI studies.

Conclusion

Community graphs are throughout us, even hiding in your relational datasets. Whereas there may be nice community graphing tooling on the market, constructing community graphs in PowerBI permits you to deliver this superior analytic device to your commonplace BI stakeholders, in addition to construct superior reporting by including context with extra filters and charts.