Exploring Network Visualization in Jupyter Notebooks

A Comparison of ipycytoscape and ipysigma


In the realm of data science and network analysis, Jupyter Notebooks have become an increasingly popular platform for exploring and visualizing complex relationships. When dealing with large datasets, we’ll compare two prominent network visualization libraries for Jupyter Notebooks—ipycytoscape and ipysigma—in the context of our use case.


Before delving into the comparison, let’s outline the specific requirements of our project. We have a vast dataset consisting of 7000 handpicked articles showcasing the versatality and impact of agent-based modeling. We need a method in which we are able to explore and analyze code sharing and model documentation practices via visualization and properties of the dataset. Our goal is to uncover and visualize the relationships between authors within this publication corpus. This involves creating a network representation where nodes represent authors, and edges indicate collaborations or shared authorship. We present a network analysis visualization tool we built which is able to stay within the existing research workflow and achieve insightful information about the dataset in a dynamic manner. We need a tool that allows users to explore the properties of and relationships among the dataset, in multiple views. This needs to be severly modified.



  • ipycytoscape is a powerful network visualization library for Jupyter Notebooks and an open-source software platform for visualizing complex networks, built on top of Cytoscape, a popular graph theory library among the computational biology community.
  • Offers a rich set of features, including customizable node and edge styling, interactive user interfaces, and support for handling large graphs efficiently


  • ipysigma, on the other hand, is based on Sigma.js, a JavaScript library for graph drawing. ipysigma provides a Jupyter widget for interacting with Sigma.js within the notebook environment. It’s known for its simplicity and ease of use, making it suitable for quick visualizations

  • With ipycytoscape, users can create interactive widgets, allowing for dynamic exploration of the network. This is particularly beneficial when dealing with large datasets, as users can zoom, pan, and filter the graph to focus on specific regions of interest



1. Robust Performance:

  • Integration with Cytoscape: ipycytoscape leverages the robust capabilities of Cytoscape, a powerful graph visualization library. Cytoscape is optimized for efficiently handling large graphs, ensuring that ipycytoscape can seamlessly manage and display substantial numbers of nodes and edges.

2. Directionality Support:

  • Graph Directionality: ipycytoscape supports the visualization of directed graphs, allowing users to represent and analyze relationships with clear directional information

3. Node Labeling:

  • Easy Node Labeling: Labeling nodes is a straightforward process, enhancing the interpretability of the graph by providing meaningful information associated with each node.

4. Node Size Customization:

  • Flexible Node Size: Users can easily adjust the size of nodes, providing flexibility in visual representation based on the importance or significance of nodes in the graph.

5. Pandas Integration:

  • Seamless Pandas Integration: ipycytoscape facilitates smooth integration with pandas, a popular data manipulation library. This integration simplifies the process of importing and working with graph data from pandas DataFrames.


1. Graph Attribute Display:

  • Visualizing Graph Attributes: ipysigma allows for the display of various graph attributes, such as graph size and node size. This feature enhances the ability to convey additional information beyond just the graph structure

2. Customization:

  • You can customize a large number of the graph’s visual variables such as: node color, size, label, border, halo, pictogram, shape and edge color, size, type, label etc.

3. Performance with Sigma.js:

  • Sigma.js Integration: ipysigma is built on Sigma.js, a JavaScript library renowned for its efficiency in handling large graphs. While performance may vary based on graph complexity and browser capabilities, ipysigma benefits from Sigma.js’s capacity to handle substantial data with grace.

4. Pandas Compatibility:

  • Effortless Pandas Integration: Similar to ipycytoscape, ipysigma seamlessly integrates with pandas, enabling users to work with graph data stored in pandas DataFrames without unnecessary complications.

Users can choose between these libraries based on their specific needs and preferences, considering factors such as graph size, complexity, and the desired features for visualization and analysis.

Namita Shah
Namita Shah
Undergraduate Student Researcher

Namita is currently a junior pursuing Computer Science at Ira Fulton School of Engineering and Barrett, the Honors College, with minors in Data Science and Educational Studies. She is also an intern for the Jupyter4Science project.