 When was the last time you checked upon your network? Maybe at a conference trying to score business cards? Or when you were browsing through your LinkedIn contacts? How does your network influence your behavior? How does your network give you power? Or block you in achieving what you want? Network analysis assumes that the behavior of actors like you depends on the structure of the actor network. Let's say we want to predict your salary. Of course, we could look at your education level or your years of working experience. But maybe it's much more insightful to look at who your close relations are in your organization and whether you can control crucial information flows between other people. With network analysis, we look at the structure of the network to explain behavior and outcomes. And that's what makes network analysis methods unique. Network analysis can be a very powerful tool to understand the behavior and strategies of actors. In this video, you'll learn for what kind of questions the method is most suitable. Let's take an example of how networks lead to innovation. I was doing a study for a Dutch energy grid operator. They are on a quest towards renewable energies and I know they cannot do it alone. So this company was heavily involved in all kinds of innovation projects with other actors. But they were confused. What's our position in this innovation network? Are we working with the right partners? What if we would end some of these projects? Would the innovation network fall apart? Network analysis helps you to answer these types of questions. Let's look at a simpler example first. Let's say you're giving a birthday party. You can only invite six people. What makes the best birthday party? If you are friends with all of them, but none of them know each other, like here in picture A? Or if all six are also friends of each other, like in picture C? Or perhaps something in between? Now picture C is what we call a dense network. All actors are related to each other. From theory we know that dense networks produce joint activities. So probably this birthday party will be the most lively. This picture A is a very sparse network. Perhaps less lively because people don't really know each other. But here you have a much better chance to hear new gossip. Because theory tells us that sparse networks carry much more new information. So how does your network at work look? What's your large project more like Network A or B? And how does it impact your work? Now there are of course a lot of other things that affect your birthday parties. The people's age, friendliness, or the amounts of jokes that they know. In other words, the properties or attributes of people. In our example, we did not consider these attributes. We just looked at relationships. And that's the core assumption of network analysis. The structure of our relationships governs our behavior. This assumption is what makes network analysis so interesting. But also fundamentally different from most other methods. And we will see that it creates quite a few challenges as well for executing the method. So, to summarize. Network analysis explains the behavior of actors by looking at their relationship structure. Not the attributes of individual actors. Actors, they can be anything. People, teams, organizations, industries, countries, ends. It's most useful in situations where actor relationships matter. Where it's about information flows between people or organizations. Power positions, clique behavior, etc. In this way, it's uniquely different method for most that you might know. And it offers great opportunities for answering your search questions. But also great challenging for collecting and analyzing data.