Create a Time Series Network Graph Visualization in Python

Unlock the power of time series network graph visualization in Python, gaining deeper insights into evolving data relationships. Step-by-step tutorial provided.

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29. Oct 2023
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Create a Time Series Network Graph Visualization in Python















Network graph representations are a potent tool in the data visualisation space for deciphering intricate relationships inside datasets. On the other hand, a time series network graph expands on this idea by allowing us to investigate complex relationships and comprehend how they change over time. We will explore the theoretical and practical procedures for visualising a time series network graph in Python in this post.

Understanding Time Series Network Graphs

Let's understand the basic principle of time series network graph creation before getting into the technical details.

In essence, a timestamped collection of nodes, which represent entities, and edges, which show connections, makes up a time series network graph. The timestamps on these edges show us how these relationships have changed over time and indicate interactions or links between entities.

Use Cases

Time series network graphs find applications in various domains, such as:

  1. Social Network Analysis: Observing the evolution of friendships and interactions on social media platforms over time.

  2. Transportation Networks: Analyzing how transportation connections change and adapt over different time periods.

  3. Epidemiology: Tracking the spread of diseases and their connections to various factors over time.

  4. Finance: Monitoring the flow of investments, transactions, and trading patterns in financial networks.

 

Now that we understand the concept, let's move on to creating our own time series network graph visualization.

Prerequisites

To create a time series network graph in Python, you'll need the following:

  1. Python Installed: Ensure you have Python installed on your system.

  2. Libraries: Familiarity with Python libraries like NetworkX for graph operations and Matplotlib for visualization.

  3. Data: Prepare your time-stamped data in a format suitable for network graph creation.

 

Steps to Create a Time Series Network Graph

Step 1: Data Preparation

Your data should include information about the entities, their relationships, and the timestamps when these relationships occurred. This can be stored in a CSV file, a database, or any other structured format.

Step 2: Importing Libraries

You'll need to import Python libraries like NetworkX and Matplotlib. NetworkX will help you create, manipulate, and analyze the graph, while Matplotlib will handle the graph visualization.

Step 3: Building the Graph

Next, create an empty graph using NetworkX. You can then iterate through your data to add nodes (entities) and edges (relationships) to the graph. Be sure to parse the timestamps into datetime objects for accurate time-based analysis.

Step 4: Visualizing the Graph

Once the graph is constructed, it's time to visualize it. You'll want to create a series of snapshots, each representing the graph at a specific time. To do this, you filter the edges based on a time window, which determines which connections are active at a given point in time. Then, plot the graph using Matplotlib, and optionally, save the visualizations for further analysis or presentation.

Conclusion

Visualisations of time series network graphs offer an engaging and perceptive means of examining changing interactions in a variety of domains. You may make your own Python time series network graph by comprehending the theory underlying these visualisations and by following the instructions provided in this article. In your journey through data analysis, this capacity may be quite helpful in revealing hidden patterns and obtaining a deeper comprehension of how connections evolve over time in your datasets.

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