Network analysis is a research approach focused on the relationships between data points. It works by breaking structures down to nodes/vertices (often these are individual people, or things within the network) and edges/links (relationships) that connect them. It has applications in many various disciplines including information science, computer science, communication, physics, electrical engineering, history, biology, economics, finance, climatology and sociology.
Networks exist in all aspects of daily life: from neural networks in the brain, to power grid networks of generators and power lines that transmit electricity, to trade networks of goods and services on an international scale, to social networks - the professional, friendship and family ties that determine the spread of knowledge, behavior and resources, to Internet. Network analysis can help study these phenomena by focusing on relationships, and not attributes of the specific objects of study.
Network analysis developed from a multitude of different subfields: mathematics (graph theory) physics, sociology, biology, computer sicence, engineering and communications.
Two of the most common approaches currently are Social Network Analysis and Historical Network Analysis.
Social network analysis is the examination of relationships between individuals, organizations and other groups that interact with each other. These networks are often visualized to help understand how they tie together and assist in drawing conclusions and raising further questions. For social network analysis you might use web scraping tools to get data from popular media websites, such as Twitter, Facebook, or Reddit. Through the use of tools such as Gephi, you can visualize and analyze this data focusing on understanding the dynamics that drive relationship or community creation.
Check out this Cheat Sheet for basic concepts related to network analysis: Social Network Analysis for Humanists
With historical network analysis, the source material is often found in archives, census tracts, or publications of ancient material. It can be very difficult to visualize how these groups interacted with and were related to one another with other methods so it is fortunate that recent developments in the digital humanities have allowed researchers to visualize these complex sets of data using network analysis.
Network analysis tools, such as Gephi, allows users to create dynamic visualizations of historical networks, tracing lineages or relationships over time.
For example, Temple Professor Marcus Bingenheimer is focusing on creating a foundational dataset for the dynamic historical social network analysis of Chinese Buddhist history. This dataset and innovative methodological approach will enable historians of East Asian Buddhism to use network analysis methods.
