Social Network Visualization Using Python

    For this assignment, I created a simple undirected social network graph using Python, NetworkX, and Plotnine. The goal was to build a small network, convert it into a DataFrame, and visualize it using a ggplot2-style plotting library.

Process & Reflection

    To start, I generated a random graph with 10 nodes using the gnp_random_graph() function in NetworkX. This part worked really well, NetworkX makes it extremely easy to build graph structures and assign attributes like labels. I labeled each node from A through J and used the spring layout algorithm to position everything. Converting the graph into two pandas DataFrames (one for nodes and one for edges) turned out to be helpful because Plotnine works best when the data is structured in a tidy format.

    The visualization step was surprisingly smooth. Since Plotnine is based on ggplot2, the syntax felt familiar: I used geom_segment to draw the connections, geom_point for the nodes, and geom_text to display each label. Once the aesthetics were mapped correctly, the layering system was very intuitive and produced a clean network graph.

    I did run into a few issues during setup, mainly installing the required packages on my Mac and making sure VS Code was using the correct Python interpreter. Plotnine in particular took a little troubleshooting. I also had a minor error at first because my column names didn’t match the aesthetic mappings exactly, which caused the plot not to render. Once I fixed those small details, everything ran without problems.

    Overall, after working through the initial installation issues, this method worked really well. I would definitely use this approach again for Python-based network visualizations. NetworkX is powerful for generating and analyzing graphs, and Plotnine gives a lot of control over the final appearance. It’s a good balance between flexibility and readability, especially for assignments where reproducibility and customization matter.

 


Comments

Post a Comment

Popular posts from this blog

Student Loan Program Trends in the United States (2007–2025)

Image
Written Report      This project examines how federal student loan programs in the United States have changed over time, with a focus on borrowing amounts, the number of student recipients, and the growing cost burden on individual borrowers. Although student debt is often discussed as a single issue, most people are not aware that federal loans come from different programs, each with its own history and trends. The goal of this analysis is to understand which programs are expanding, which are disappearing, and whether students today are receiving more borrowed dollars per person than they did in the past.      The idea for this project was influenced by several existing visual resources, including the Federal Student Aid dashboard, DataUSA’s educational finance reports, and journalist-designed college affordability graphics. These examples revealed how powerful visual storytelling can be in making complex financial data easier to understand. However, man...
Read more

My Geographic Map of Florida COVID-19 Cases by ZIP Code

Image
     For this week’s assignment, I was instructed to find a dataset and create a visual using Tableau. I selected the Florida COVID-19 Cases by ZIP Code (April 28, 2021) dataset from the University of South Florida Libraries’ Digital Heritage and Humanities Collection, originally downloaded from the Florida Department of Health ArcGIS Data Portal. Using this data, I wanted to examine how COVID-19 cases were distributed across Florida ZIP codes during the s pring of 2021. After importing the dataset into Tableau, I generated this geographical map using a blue color scale , since it provided the clearest contrast.      On the map, ZIP codes shaded in darker blue represent higher case counts, while lighter areas represent lower counts. Unsurprisingly, South Florida ZIP codes (especially in Miami-Dade and Broward counties) had the highest reported numbers. Central Florida areas around Orlando also showed clusters of elevated case counts. What stood out most w...
Read more

Spotting Differences in R: Comparing Fuel Efficiency by Cylinder Count

Image
  For this assignment, I used RStudio and the built-in mtcars dataset to explore differences in average miles per gallon (MPG) across cars with different numbers of cylinders. Using ggplot2 , I created a simple bar chart that displays the mean MPG for 4-, 6-, and 8-cylinder cars. The chart clearly shows that 4-cylinder cars achieve the highest average fuel efficiency , followed by 6-cylinder, and then 8-cylinder vehicles with the lowest MPG. This visualization directly supports a spotting differences task, it makes the contrasts between groups immediately visible through bar height comparisons along a shared baseline. From a design perspective, I applied a minimal theme and used a clear title and axis labels to keep attention on the comparison itself. This aligns with Stephen Few’s  principle of simplifying visuals so that viewers can detect differences quickly and accurately. Likewise, Yau  emphasizes using straightforward encodings, like length along a common scale...
Read more