How to Create Network Graph Visualizations in Microsoft PowerBI

Microsoft PowerBI is a probably the most well-liked Business Intelligence (BI) instruments, and whereas it has all of the options you could create dynamic analytic reporting for stakeholders throughout the enterprise, creating some superior knowledge visualizations is more difficult.

This text will stroll by means of methods to create massive community graph visualizations in Microsoft PowerBI to allow dynamic and interactive exploration of interconnected datasets reminiscent of provide chain networks, monetary transactions, and far 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 characterize discrete issues and edges characterize the relationships between nodes.

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

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

A easy community of three profiles would possibly find yourself wanting like this:

When visualizing community graphs, we are able to embed further details about nodes and edges in numerous methods, reminiscent of however not restricted to:

1. Node measurement
2. Edge measurement
3. Node coloration
4. Edge coloration
5. Labels

Structuring Community Knowledge

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

Graph Knowledge is All over the place

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

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

We will characterize this similar gross sales desk as a community graph by representing each product identify because the node sort “product”, buyer identify because the node sort “buyer”, and every row as the sting “Bought”.

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

Graph Knowledge Codecs

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

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

However on this article, we can be utilizing a mixed node and edge listing 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 Shade -> It is a class identifier for the supply node (for instance, Buyer Sort)
4. Goal Shade -> It is a class identifier for the goal node (for instance, Product Class)
5. Hyperlink Shade -> It is a class identifier for the sting (for instance, Gross sales Channel)

Creating the Community Graph Visualization

Now that we have now our knowledge mapped, we are able to create the community graph visualization. 

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

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

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

Drag the visible onto your canvas, choose it, and word the values required (which we mapped earlier). The visible additionally has choices to move x and y coordinates in addition to customized labels, nevertheless we gained’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 info pane.

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

To alter the simulation settings, open the formatting pane, then simulation, and enhance each the hyperlink distance and repulsion drive. 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 further info into the graph, by altering the node coloration based mostly on node classes. Drag the fields you mapped above to Supply Shade and Goal Shade.

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

 Let’s do some formatting to the background coloration 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 measurement
• 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 we have now the visualization, stakeholders have to make use of it to make extra knowledgeable selections.

Interacting with the Community Graph

There may be instant worth in a static community graph, reminiscent of having the ability to visually see how knowledge is interconnected by means of relationships. 

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

First, we are able to work together with the legend by deciding on classes to spotlight them on the graph. For instance, shortly finding Widgets within the graph:

We will additionally choose particular person nodes within the graph by clicking on them.

Alternatively, you possibly can toggle “choose adjoining nodes” within the node properties to have it choose not simply the node clicked on, however all nodes straight related to it by means of an edge.

For instance, deciding on “Widget A” with “choose adjoining nodes” on reveals 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 are able to add further charts to present some extra context to the person’s choices. 

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

We will additionally do the reverse by filtering the info going into the community visible. This may be completed in a number of methods, reminiscent of:

1. Slicers
2. Deciding on items of different charts, reminiscent of a slice of a donut chart
3. Filter pane

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

Constructing Advanced BI Studies

Whereas the instance community graph on this article is comparatively easy for demonstration functions, you possibly can construct fairly advanced BI reporting for stakeholders. 

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

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 lets you carry this superior analytic instrument to your commonplace BI stakeholders, in addition to construct superior reporting by including context with further filters and charts.