This article explains the Social Network Analysis in a practical way. After reading it, you will understand the basics of this useful social network strategy.
What is a Social Network Analysis?
Social Network Analysis (SNA) is the mapping and measuring of relationships and streams between people, groups, organisations, computers, URLs and other sources of information that are connected. Management consultants in particular use Social Network Analysis to map their business relations and further investigate their mutual relationships. This can be compared to a central nervous system that connects everything.
A Social Network Analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Emile Durkheim. They wrote about the importance of controlling social network patterns. In the early 20th century, social scientists already used the concept of ‘social networks’ to refer to complex relationships between members of social systems on all scales, from interpersonal to international. The use of systematic social network analysis was later expanded. American professor of organisational science David Krackhardt, for example, explains the network visualisation software, developed by him, in his book ‘KrackPlot’. This software was designed for the analysis of social networks and is often applied in academic research. In doing so, the KrackPlot software creates the foundation for a Social Network Analysis.
Both Social Network Analysis and KrackPlot use a simple, screen-oriented interface that allows for the easy transfer of nodes with the computer’s mouse after which new nodes can be added. Several characteristics can be appointed to each node. Subsequently, these can be flagged by means of colour and shape. KrackPlot automatically allocates colours and shapes based on the characteristics people have included beforehand. The nodes in the network are the people and the groups. The connections between the nodes represent the mutual relationships and/or streams. As a result, SNA isn’t only a mathematical, but also a visual analysis of human relationships.
The connection between the nodes, and therefore the interaction between social networks, can be measured on three levels within a Social Network Analysis:
1. Degree Centrality
These are the persons who have the most direct connections within the network. They are the most active. They form the central links in a social network. Degree centrality concerns where the connections lead and how others are connected. This person is also referred to as the ‘connector’ or the ‘hub’. He is an individual networking centre who offers insight into his specific location within the network. This location might reveal a lot about the general network structure. A hub can fail abruptly when it’s disabled or removed. This makes the hubs highly important nodes with a high degree.
2. Betweeness Centrality
These are people in the social network who literally stand in between others. Although some of them are directly connected to hubs, others have little direct connections. Nevertheless, these people often occupy one of the best locations in the network. After all, they are located between two important hubs. They fulfil a so-called ‘broker’ role in the network; on the one hand, they are powerful and on the other hand, they’re dependent upon others in the network. Because they’re literally in between, they can make sure that hubs don’t receive information. This makes their location invaluable within the network.
3. Closeness Centrality
Finally, there are the stragglers within a network. They are at the end of the line and have direct and indirect connections to the hubs and the ‘between’ links. This means they’ve got access to all nodes in the network faster than anyone else. After all, they can take the shortest routes to all other people in the network. From their position, they have a good overview and are able to closely keep an eye on the information stream in the network and see what changes.
In the process of studying the social structures, Social Network Analysis uses graph theory. This is a sub-area of mathematics that studies the properties of graphs. A graph consists of a collection of dots that are called nodes. These nodes are connected by lines, arrows or arches, for instance. Complex networks apply lots of graphs. Instead of nodes, SNA represents social relationships between individual actors, people or things within a network and connects the mutual relationships. Subsequently, these networks are visualised, where nodes are represented as dots and the mutual connections and relationships are represented as lines. A few examples of social structures include:
- Social media networks, including Instagram, Twitter and Facebook
- Knowledge and professional networks, including LinkedIn
- Spread memes – a meme is a term from memetics. This concerns an idea that spreads via information carriers. Think of the human brain, but also of the current social networks, for instance. In this way, information spreads at lightning speed and is therefore described as an ‘infectious information pattern’.
Not all network paths are of equal length. Exerting influence in a social network isn’t solely dependent upon a large range. Research shows that shorter paths in a network are also very important. It’s particularly important to know who are part of a network environment, who knows who and how people can reach each other. LinkedIn makes clever use of this.
It’s often supposed that all information streams via the shortest paths in the network. However, networks run via both direct and indirect paths, both short and long. Interesting information might reach us from highly diverse and sometimes surprising sources. Therefore, it’s essential that you move on many paths and expand the network step by step.
The previously mentioned hubs who are connected to others in their social network, who are held in high regard. They occupy key positions within the social network and form a bridge between multiple different network. These hubs have access to many new ideas and information that enter into other networks. Thanks to their strategic position, they can combine all this information with new products and services and put other people into contact with each other.
It’s Your Turn
What do you think? Have you ever heard of a Social Network Analysis? Do you recognize the practical explanation or do you have more suggestions? What are your success factors for mapping and measuring of relationships and streams between people, groups, organisations, computers, URLs and other sources of information that are connected?
Share your experience and knowledge in the comments box below.
- Freeman, L. (2004). The development of social network analysis. A Study in the Sociology of Science, 1.
- Krackhardt, D., Blythe, J., & McGrath, C. (1994). KrackPlot 3.0: An improved network drawing program. Connections, 17(2), 53-55.
- Tichy, N. M., Tushman, M. L., & Fombrun, C. (1979). Social network analysis for organizations. Academy of management review, 4(4), 507-519.
- Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.
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