Social Network Analysis

Imagine you're at a large conference, filled with a bunch of people. As you walk through the event and visit different rooms, you notice clusters of people invested in conversations, isolated individuals looking around, influential speakers drawing in crowds, and the event organizers making sure everything’s going according to plan. 

Every interaction, each connection, and each group dynamic is part of a larger and more complex web. This is, in a nutshell, what social network analysis (SNA) seeks to explore.²

SNA provides a way of visualizing relationships and their complex interplay by incorporating both quantitative and qualitative data. It aims to understand how different actors work together and share knowledge or resources across a network. From an organizational standpoint, SNA can help understand how information flows within a firm, identify key influencers in a social media community, or map the collaborations between scientists.

In essence, SNA involves creating a network map or graph where nodes represent actors (people, organizations, etc.) and the connecting edges or lines represent the relationships and interactions between them. Once these maps are drawn, analysts can find patterns such as clusters, central nodes, and bridges that link different parts of the network.²

How a Network Map Works

Going back to our conference scenario, let’s break down how a network map works. In a network map, each person at the conference is represented as a node (circle), and each interaction between them is represented as an edge (line). Thicker edges denote stronger relationships, such as long-term friends, while thinner edges denote weaker or newer connections.

1. Nodes: Let’s say, Alice, Bob, Carol, David, Eve, and Tom are attendees (dark green). Emma and Liam are organizers (green). And Mike, John and Sarah are speakers (teal).
2. Edges: The edges represent the interactions or relationships between nodes. Thicker lines represent stronger relationships. For example between Sarah and Mike, and Mike and Bob. Sarah and Mike know each other from work, and Bob and Mike are high school friends.

By analyzing this map we can draw some conclusions. Sarah, Bob and Alice are known as high-degree nodes as they have the most interactions. In other words, they are key actors (or central figures). Organizers are well-connected and collaborate with one another. 

Tom is a bit of an isolated person who has only interacted with the organizers. Tom might benefit from being introduced to other attendees by his established connections, but we would need more information. Understanding these dynamics in this scenario could help plan more effective networking strategies for future conferences.