Network Analysis

Network Analysis

Network Analysis Resources

Welcome to the Berea College Hutchins Library network analysis resource guide. 

What is network analysis?

Network analysis surveys the relationship between different entities, such as collaboration between researchers, interactions between genes, or communications between a people in a company. It can be utilized for a variety of purposes, from simply studying the structure of a community to solving complex math and engineering problems through graph theory.  Along with this, network analysis can also examine the relationships between publications based on authorship, citations, standard terms, the spread of information, and even memes! 

A network is simply several points (or ‘nodes’) that are connected by links. Generally, in social network analysis, the nodes are people, and the links are any social connection between them – for example, friendship, marital/family ties, or financial ties.

Types of networks: (Halgin & Dejordy 2008)

  • Socio-centric used when analyzing the different patterns of interaction within a defined group. 
  • Egocentric is used when a research question examines a phenomena affecting individual entities across different settings. 

Network visualization is the visual component of network analysis. There is a wide range of network visualization to choose from depending on the kind of data you have available or what types of relationships you want to see and show. 

The network analysis process requires:

  • Defining your research question
    • Construct an academically driven question that refers to a connection of entities in some fashion.
      • Ex. 1. I want to know how social relationships contribute to the construction and maintenance of an individual's nutritional health. 
      • Ex. 2. I want to analyze how restrictive censorship on Twitter has contributed to the rising pattern of equitable language on the social network site. 
      • EX. 3. I want to explore the amount of flow of information that was available to the Hong Kong protesters on social platforms in 2019.
  • Identifying the kind of data needed for your analysis
    • Who, what, and how much data is available to the information that I want to survey?
      • Relational data: revealing some kind of connection between individuals, institutions, or products. 
        • Co-occurrence (same organization, same school, etc.)
        • Distance (number of miles between, etc.) 
        • Actions (talk with, meet with, collaborate with, eat with, etc.)
        • Resource (knowledge, facility access, resource access, etc.)
        • Affective (like, dislike, respect, etc.)
        • Kinship (e.g., sister, brother, cousin, etc.)
        • Social roles (supervisor, teacher, friend, acquaintance, etc.)
      • Existing data:  public datasets on organizational connections, data on social media connections, datasets from CRMs (like Salesforce products (commodities, stocks, currencies), and your own existing knowledge on the matter.
      • Binary Data: 
      • Interval Data:
  • Select data collecting tools 
    • A survey should include questions regarding the background of the respondent and a way for them to provide information on connections.
  • Select data collecting methods
    • Full Network Method: used when collecting data from every member of your network (or network subset that you are investigating). This method works with a bounded network. You may not be able to get everyone, but the more people you get, the more complete your understanding of the network will be.
    • Snowball Method: used when starting with a core group of network members, you collect data on all of their connections. Then you reach out to the new connections and collect data on all of their connections. This continues until you cannot surface any more new members or until you run out of time. This method will miss members who are not connected to the people sampled and may bias your sample; on the other hand, it may also help you access a wider sample of network members than you could have identified on your own.
  • Analyze data 

    • ​You can analyze the collected or existing data by using a network visualization tool.



  • Gephi Gephi is a free, open-source desktop visualization tool that specializes in network visualization and analysis.
  • NodeXLNodeXL is an add-on to Microsoft Excel with a free plan. It allows you to create various node-link diagrams using Excel spreadsheets. Unfortunately, it is only compatible with Microsoft Windows versions of Excel.
  • RAWGraphs - RAWGraphs is a web-based tool that creates visualizations from copied and pasted data or uploaded files. Made with D3, RAWGraphs is not entirely focused on network visualization. However, it does have a number of options for visualizing hierarchical data and flows.
  • VOSViewerVOSViewer is a network visualization tool specifically developed to aid in examining bibliometric networks, such as collaborations between researchers and relationships between publications.

Readings and Online Resources:

Meho, L. I. (2007). The rise and rise of citation analysis. Physics World, 20(1), 32-36.

Chen C., Song M. 2017. Science Mapping Tools and Applications. In: Representing Scientific Knowledge. Springer. Available at

Shivraj Pawar, Rutuja. “Scholarly Network Analysis (SNA) An Introduction to the Study of Scientific Research Networks.” Towards Data Science, August 18, 2019.

“Social Network Analysis: An Introduction.” Social Network Analysis: An Introduction by Orgnet, LLC. Accessed January 2, 2020.


“PPT.” Los Angeles, CA, n.d.

“PDF,” n.d.


Manuel Lima's Visual Complexity: Lots of examples here, based on Lima's Book of Trees: Visualizing Branches of Knowledge (2013) and Visual Complexity: Mapping Patterns of Information (2011).

Albert-László Barabási's Network Science (2015).  A textbook companion with explanations, examples and tools.

Star Trek Viz: An example of a social network interface based on the fictional Star Trek series and films.

Music Map: An interactive tree graph of the relationships between music genres.

Kindred Britain: Stanford project showing kinship connections between people in British history.

Game of Thrones on Twitter: An example of community detection using Twitter posts and Wikipedia recaps for Game of Thrones.  The explanation of the data viz is here.

Black Shoals Project: A 'planetarium' that turns companies, their stock prices and relationships into a starry sky visualization.

Edward Tufte: The doyen of data visualization's website.  He is famous for his trilogy of books including "Envisioning Information."

"Fifteen Theses on Contemporary Art" by Alain Badiou: Begins with a discussion of Mark Lombardi's work on visualizing covert networks and a high quality image of a network he drew multiple times between 1979 and 1999.

Hans Haacke: 1971 artwork "Shapolsky et al. Manhattan Real Estate Holdings, a Real-Time Social System, as of May 1, 1971" showing Manhattan real estate in relation to developers as a system.

Ed Ruscha, "Every Building on the Sunset Strip" (1966): An example of archival art, attempting to capture the street at a moment in time.

Charles and Ray Eames, "Powers of Ten" (1977): A short film using cascading images to show the relative size of the universe.

* Adapted from GSU's Guide for Data Visualization Tool

For Additional Help

If you are assigned a multimedia/new media project for your class or are interested in learning digital tools and methods for your own personal use, schedule a consultation with the Digital Initiatives Librarian on the Digital Initiatives homepage.