No common nodes between Three centrality measure of PPI network in top 200 nodes

Question

[beingseema03]

Hi all,

I am using mentha database for analyzing PPI networks. I have find the three centrality measures Degree,Closeness and betweenness centrality. Then I have sorted all three centralities from low to high. And now I am considering top 200 nodes. My network consists 16,000 nodes and 1,73000 interactions. Then I tried to find the common nodes between all the three centralities top 200 nodes. But the intersection between these three centrality results zero. That means the results are showing that there is no such common node between these three centralities. Is this kind of result is possible..?? And there is also no common node between closeness and betweenness and 4 common nodes between degree and closeness from top 200 nodes. So this kind of result is possible..?? This is a very rare result.

[Martombo]

I think this is possible, in the end you are looking at very few top nodes (200 / 16000). if you want to check if you did everything correctly, why don't you plot the centrality measures of all nodes together. I think that in general, at least for closeness and betweenness, there should be some correlation.

[beingseema03]

Thanks for your reply.. Will try out that way

Answer 1

[Jean-Karim Heriche]

Although centrality measures tend to have positive correlations with each other, these measures all give different information so you shouldn't expect them to be high/low for the same proteins. The degree is the number of interactions that the protein makes, the closeness centrality measures how many "hops" in the network the protein is to other proteins and the betweenness centrality measures how often a protein is seen on the shortest path between other proteins. Imagine that the network represents relationships between people. If you want more people to buy you a drink, you should maximize your degree centrality i.e. have lots of friends. However, if you want to make sure that you know about every gossip making the rounds then you should maximize your betweenness centrality because having many friends doesn't help if they're isolated. Now if you're a lobbyist, you want to be able to talk to a wide range of people so you should maximize your closeness centrality.
There are other measures of centrality too that report on other properties of the nodes. Which centrality measures are relevant depends on the question you're asking about your nodes/network. For example, proteins with high degree but low closeness may form an isolated complex. Or high closeness and low betweenness could point to possible interaction "contaminants".

[beingseema03]

Hello jean thanks for such a great explanation.