We believe that by itself, data offers finite value. But when interconnected? The value becomes infinite. According to Metcalfe, increasing data’s connectedness further increases its value through additional context as each new user in a network enhances the value exponentially. Network Analysis is a technique used to analyze connected data that can help solve some common data science problems. It can also be used for visualizing networks at a much grander scale, making it a very important concept across various organizations.
Companies are interested in integrating machine learning algorithms and building other AI-based systems that help uncover a variety of data and insights that can improve day-to-day operations. One critical requirement before improvements can be made is to understand how pieces are connected, but networks are often hard to digest. By visualizing the network, data becomes more readable for customers, and insights seem more intuitive. Often, the story will tell itself with this type of visualization.
What is Network Analysis?
Network Analysis is a powerful technique to quantify the interconnections of data, and the visualization of the results provides a readable way to interpret them. By looking at this analysis visually, the user can observe and understand the relationship between different data points. Each node within the graph represents a data point or entity, and these nodes are then connected via links or edges. Visualizing in this way helps the data to tell its story as spotting patterns, validating outliers, and drawing insights become easier for the end user.
Customize to deliver relevant insights
One of the benefits of using network analysis graphs to represent data is that the graphs are customizable to allow for different insights based on context. Graphs can include arrows to indicate the source node from the sink node, or you can have no directionality depicted. Nodes can be sized uniformly or based on various criteria (e.g. the number of downstream connections or the total cost directly associated with that node). Similarly, edges can be weighted based on any criteria you may have. Most often, the thickness of the line relates to the number of occurrences or the value flowing between the two nodes. The ability to differentiate clusters by categorically assigning colors or using a gradient based on a measure, such as number of members, is critical in insight generation. Nearly every element of the graph can have their graphical properties tailored to the scenario, and more than one network graph may result from a single analysis.
Algorithm selection can also drive insights
Along with directionality and weights, an additional parameter that is important to your analysis is centrality. Centrality algorithms allow the user to find the most important nodes in the network at a glance. Just like different notions about what should be weighted, there may be different variations as to what an important node could be. This leads to many centrality measures. The three most common are degree, closeness, and between centrality. Algorithm selection will depend on how you want to use your central nodes. Finding influencers, or those who can quickly connect with the larger network, will require a different algorithm than finding the source of a cluster.
Popular packages to draw network graphs
Network Analysis graphs are a very useful and robust visual that can be generated by many languages and tools. With R, the igraph library is common, while in Python, networkx is what we recommend. With help from additional programming, both Alteryx and Tableau can showcase a network analysis. In fact, Alteryx will use the igraph library to generate a dynamic graph. The different available tools and wide range of insights of network graphs allow them to help organizations make insightful decisions. For more information on how to use Alteryx and Tableau for this particular use case, look for the continuation of this topic in the coming weeks.
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