Social network analysis or also known as social network analysis (SNA) is a research method with a network-based structural approach.
As a research method, social network analysis is applied in multidisciplinary research. Not only in the social sciences, but also in non-social fields.
Research Methods: Definition, Types and Examples
We will discuss what social network analysis (SNA) is in the context of social science research. The main characteristic of the SNA method is the network or networks. Here we need to understand what a network is and how it differs from other similar terms, for example relations or relationships.
Definition of social network analysis
SNA is a method of structural data analysis. The structure referred to more specifically is a network.
Networks are actor interrelations. There are two main components of the network here, namely the actors and their relationships.
In social science, the actors in question are not limited to humans. Actors can be in the form of location, ideology, preferences, behavior, emotions and other non-human aspects.
Relationships can also take many forms. Not limited to acquaintances, cooperation or conflict. Relationships can also be in the form of work relations, blood relations, bilateral relations and so on.
Even in the context of digital social research, relationships can be in the form of mentions, tweets, comments and so on as they appear on digital platforms.
At this point, we first understood that actors and relationships are not limited to humans and their interactions.
The SNA method is very concerned with the computational, calculation or quantitative aspects. Let’s start with a simple example. Relationships between humans that consist of two different actors are different from that of three or more actors.
Different relationship characteristics have implications for different social conditions. A society can be solid or messy because of the implications of the different characteristics of social relations.
A network is a collection of actors and their relations, where quantitatively, the number of actors is more than two and the number of relations is more than one. Consider the following image example:
The picture on the right is a picture of the relationship between A and B. While on the left is a network of C, D and E.
The difference between a relationship and a network needs to be emphasized. One of the sociologists who is concerned with this is Georg Simmel.
Georg Simmel
According to Simmel, a relationship between 2 people has very different social consequences from a relationship between more than 2 people. Think about playing chess and playing soccer, when one of the players has to be kicked out of the game.
The game of chess could not be continued, while soccer could still be continued. That is the basic understanding of networking.
Now, the term network is widely used by social media platforms. We often identify Facebook with social networking. There is nothing wrong with Facebook as an interaction space that exhibits social networking in the form of interactions, friendships, advertisements and so on.
However, the notion of networking is not that narrow. Networking has a broad meaning. Even in social science, the meaning of networking is very broad.
What can be done by applying social network analysis methods? In social science there are many benefits. I explain the simple ones through examples.
SNA is like a property business. The closer to the city center, the more expensive it is. The meaning is increasingly important. Buy a house on Jl. Jend. Sudirman is more expensive than Jl. Turtledove. Because Jl. Jend. Sudirman is in the city center.
So it is with networking. The more actors approach the center of the network, the more valuable, influential or popular than other actors. In simple terms, actors who are in a central position are influential actors.
Those on the fringes, on the other hand, have little, if not all, influence.
Determining who is an influential actor can be done in two ways in SNA. First, identify the visual network. Second, perform a computation called Betweeness Centrality or intermediate centrality. Consider the following examples:
From the visuals of the network, we can tell that actor E is the most influential actor. His Betweeness Centrality score is definitely the highest compared to other actors.
Why is E most influential? We imagine if actor E is eliminated or removed from the network, then actor A and B will not be connected to actor C and D. The two of them will become a separate group from each other.
If we imagine them as actors of the protest movement and their relations are acquaintances planning the action, then there are 5 people who will protest. When actor E is removed, the protest action may not be continued because there are only 2 groups left where they don’t know each other.
If actor B is eliminated, the protest movement can be continued with 4 people.
SNA can reveal who are the most influential, popular, interactive actors and so on. SNA research for social sciences is able to reveal the role of actors in networks.
