When it comes to social networks, there are four main node centrality measures that describe networks. Node centrality measures are important in social network analysis, but also in other fields when doing research. Degrees look at how many links a node has to other nodes in a network. Betweenness centrality looks at the shortest path between nodes. Closeness centrality refers to how close a node is to other nodes in a network, and eigenvector centrality looks at how important a node is, it looks at how many other links to other nodes in a network there are. If a node has a high eigenvector centrality, it is well connected to other nodes, it is important.
This is a good example of how people are the nodes, it shows the links between all of the nodes in this particular network. Some are closer together than others.
Node centrality measures are not only used when looking at social networks, they can be used in the health field also when looking at infectious diseases and how they spread. “In epidemiology, some possibly infective contacts between individuals are long term (friends, family) but many are fleeting (people in the street or the market place).”(Hyoungshick & Anderson, 2012) When it comes to infectious diseases, it is important to find the node that had the most contact with others, they may be long term and people they have been in close contact with over a period of time, or they may be short term, If someone is sick on an airplane, and they touch their tray, then it doesn’t get cleaned completely, the next person who sits there could get sick. They didn’t have a long-term connection to each other, in fact, they didn’t really have a connection at all, apart from the airplane tray. In the article by (Hyoungshick & Anderson, 2012), they looked at merchants and villages and the closeness and betweenness values between them to look at how diseases spread.
This picture shows an initially infected adult and the different links it has to other nodes(people). It is a good way to show how diseases can spread. http://www.homelandsecuritynewswire.com/dr20150831-new-research-aims-to-slow-the-spread-of-infectious-diseases
In cases of Ebola or STD’S it is important that they find who came into contact with the person who is sick. The same goes for vaccinations and how they protect against disease. “If a node has twice the neighbors of another, it has twice as many nodes to which to spread an infection.” (Holme, 2017) Meaning they not only have twice the way to spread an infection, but also get an infection. If someone has a close degree centrality and they are sick, it means that they are looking at all of the links one person has to another (who they have come in contact with) When looking at closeness centrality, this looks at how close a person is to other people. Betweenness centrality would look at the shortest path between two people, and eigenvector centrality looks at how important one person is in the network and whether they had come into contact with a lot of people. If they had, this means they are a significant person, they might be a carrier of the disease. This shows that node centrality is important, not just with social networks, but even when health professionals are looking at the spread of disease.
Holme, P. (2017). Three faces of node importance in network epidemiology: Exact results for small graphs. Physical Review E, 96(6). doi:10.1103/physreve.96.062305
Hyoungshick, K., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E,85(2). doi:10.1103/physreve.85.026107