How do you handle large or complex network data sets?

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Network graphs are a powerful way to visualize the relationships and patterns in complex data sets. They can help you discover hidden connections, identify clusters, and measure centrality. But how do you handle large or complex network data sets that may have thousands of nodes and edges? In this article, we will explore some tips and techniques for network graph interpretation in the context of data visualization.

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Bill Shander

Keynote speaker, workshop leader, LinkedIn Learning Instructor focused on information design, data storytelling &…

1 Choose the right layout

The layout of a network graph affects how easy it is to understand and analyze. Different layouts emphasize different aspects of the network, such as distance, direction, or hierarchy. You should choose a layout that suits your data and your research question. For example, if you want to show the geographic distribution of nodes, you can use a map-based layout. If you want to show the hierarchical structure of nodes, you can use a tree-based layout. If you want to show the similarity or dissimilarity of nodes, you can use a force-directed layout.

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Bill Shander

Keynote speaker, workshop leader, LinkedIn Learning Instructor focused on information design, data storytelling & visualization, and creativity
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Network diagrams are notoriously difficult to make work with large datasets. Massive knotted hairballs don’t make data more clear! The most important thing is to try different methods and techniques and look at the diagram and ask yourself whether it honestly reveals the insight. If not, keep trying!

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2 Filter and highlight

Large or complex network data sets can be overwhelming and cluttered. To make them more manageable and readable, you can use filtering and highlighting techniques. Filtering allows you to remove or reduce the nodes and edges that are not relevant or important for your analysis. Highlighting allows you to emphasize or color the nodes and edges that are of interest or significance for your analysis. For example, you can filter out the nodes and edges with low degree or low centrality. You can highlight the nodes and edges with high degree or high centrality.

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Bill Shander

Keynote speaker, workshop leader, LinkedIn Learning Instructor focused on information design, data storytelling & visualization, and creativity
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You can also consider other visualizations like hive plots, which can sometimes reveal insights that a node link diagram might obfuscate.

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3 Use interactive features

Interactive features can enhance your network graph interpretation by allowing you to explore and manipulate the data in real time. You can use interactive features such as zooming, panning, dragging, hovering, clicking, or searching. These features can help you focus on specific parts of the network, reveal additional information, or change the perspective. For example, you can zoom in and out to see the details or the overview of the network. You can hover over a node or an edge to see its attributes or metrics. You can drag a node to reposition it or adjust the layout.

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4 Compare and contrast

Comparing and contrasting different network graphs can help you identify the similarities and differences in the data. You can compare and contrast different network graphs by using side-by-side or overlay methods. Side-by-side methods allow you to see two or more network graphs next to each other and compare their features or metrics. Overlay methods allow you to see two or more network graphs on top of each other and compare their overlaps or gaps. For example, you can compare and contrast the network graphs of different time periods, groups, or scenarios.

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5 Use metrics and statistics

Metrics and statistics can help you quantify and evaluate the characteristics and properties of the network graph. They can provide you with numerical summaries or indicators of the network structure, dynamics, or performance. You can use metrics and statistics such as degree, centrality, density, modularity, or clustering coefficient. These metrics and statistics can help you measure the importance, influence, connectivity, or cohesion of the nodes and edges in the network. For example, you can use degree to count the number of connections a node has. You can use centrality to rank the nodes by their prominence or power in the network.

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6 Tell a story

The ultimate goal of network graph interpretation is to tell a story that answers your research question and communicates your findings. You should use the network graph as a visual aid to support your narrative and argument. You should also use clear and concise language, labels, captions, and annotations to explain the network graph and its meaning. You should highlight the main points, insights, or implications of the network graph and relate them to your data and your context. For example, you can use the network graph to show how a social network evolves over time, how a disease spreads through a population, or how a rumor diffuses through a media platform.

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How do you handle large or complex network data sets?

1

2

3

4

5

6

1 Choose the right layout

2 Filter and highlight

3 Use interactive features

4 Compare and contrast

5 Use metrics and statistics

6 Tell a story

Data Visualization

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How do you handle large or complex network data sets?

1

2

3

4

5

6

1 Choose the right layout

2 Filter and highlight

3 Use interactive features

4 Compare and contrast

5 Use metrics and statistics

6 Tell a story

Data Visualization

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