Social network analysis Social network analysis is the study of how people, groups, organizations, or even things are connected through networks of relationships. In this kind of study, a network is made up of nodes (the points) and edges (the lines that connect them). A node can represent a person, a group, or even an organization, while the edges show the type of connection between them. These connections can represent many things, such as friendships, family ties, advice, business deals, online communication, or even rivalry.[1][2] Social network analysis helps researchers understand both the big picture of how the entire network is shaped and the smaller details of where each individual sits in the network. This makes it possible to study power, influence, cooperation, and patterns of communication in new ways.[3] Unlike traditional research methods that focus on individuals by themselves, social network analysis looks at the relationships between them. People are not treated as completely independent, because one person’s behavior often affects another’s.[4] For example, in the study of diseases, researchers use network analysis to see how illnesses spread from person to person through contact.[5] In organizations, it can show whether communication between teams is smooth or whether one person acts as a bottleneck by controlling most of the information.[6] In criminology, it is used to map out criminal groups and identify the people who are the most important for coordination.[7] Social network analysis also uses mathematical tools and visuals to explain networks. Some important measures include degree centrality, which counts how many direct connections a node has, and betweenness centrality, which shows how often a node lies on the shortest path between others, meaning it controls the flow of information. Closeness centrality looks at how quickly a node can reach others, while eigenvector centrality gives more importance to nodes connected to other highly connected nodes. These tools help researchers identify leaders, gatekeepers, brokers, and even isolated individuals. Networks are often drawn as graphs with dots (nodes) and lines (edges), which makes it easier to see complex patterns at a glance.[8][9] This type of analysis has many uses outside of studying people. In biology, it helps scientists study how animals interact, like how primates form dominance hierarchies or how dolphins hunt together. In computer science, it is used to study the internet and social media networks like Facebook, Twitter (X), or LinkedIn, where millions of nodes represent people or websites and edges show friendships, hyperlinks, or messages. Political scientists use it to understand alliances, lobbying groups, and global relations, while economists use it to study trade and financial networks that link countries together. One of the most important lessons from social network analysis is that a node’s position in the network can matter more than its personal traits. For example, a person with little money or status can still be influential if they are connected to many people or if they connect two groups that would not otherwise interact. This has been especially useful in marketing, where companies rely on “influencers” to spread messages and products. Social networks also often show special patterns, such as the “small-world” property, where any two nodes can be connected in only a few steps, like the idea of “six degrees of separation.” Many social networks are also “scale-free,” where a few nodes have a very large number of connections while most have very few, creating powerful hubs. Because of its ability to reveal hidden structures, social network analysis has become one of the most important ways to study complex systems. It brings together ideas from sociology, anthropology, math, statistics, and computer science, making it a very interdisciplinary field. With the rise of big data, machine learning, and digital records of interaction, researchers can now study networks with millions or even billions of connections. From tracking how diseases spread, to mapping terrorist groups, to studying online communities, to improving teamwork in businesses, social network analysis provides powerful tools to understand how connections shape the world. References
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