I have a skepticism about the betweenness centrality in this lecture cause the CB(3) in undirected condition would be 6 but you mentioned it as 0.6. I hope you will mend it.
Wouldn't the undirected graph be /7 and a directed graph be /10. Given the undirected edges would be counted up to 7 and for it to be 10 it'll have to include the directional aspect
Node 3 can be in a maximum of 10 shortest paths (Assuming the graph to be undirected) i.e., the shortest path of all possible pairs except for Node 3 which are, 1. Node 1 and Node 2 2. Node 1 and Node 4 3. Node 1 and Node 5 4. Node 1 and Node 6 5. Node 2 and Node 4 6. Node 2 and Node 5 7. Node 2 and Node 6 8. Node 4 and Node 5 9. Node 4 and Node 6 10. Node 5 and Node 6. If Node 3 happens to be in all of them, it will attain the highest betweenness centrality of 1. If it is in none of the shortest paths then it will get the lowest value of 0 or otherwise, anywhere between the two.
can you name an example for a network, where betweenness would be a good fit (or even a better fit than closeness / degree) to measure the importance of a node in the network?
Betweenness centrality offers a more in-depth centrality or importance of a node. A node with more connections will have high degree centrality like a hub in a community. This is irrespective of the fact that those nodes may be of least importance with only few other connections. So, it is computationally efficient but not very reliable. Closeness centrality of the nodes that may have fewer connections but their connections link them to diverse communities. These nodes are comparatively more important as they get information from different communities. There can be multiple nodes with high closeness centrality. It offers more information in comparison to degree centrality but is computationally more expensive as well. Betweenness centrality of a node is the special kind of closeness centrality where the node acts as the only door-way to a different community. Such nodes are absolutely vital as losing them would shutdown the inflow of information from one or more communities. So, they not only link multiple communities but are often the only source to them. Again, more information but computationally more expensive. This is a very useful resource on the topic: www.sciencedirect.com/science/article/pii/B9780128016565000214
The video on betweenness centrality is redone for more clarity and detailed evaluation.
th-cam.com/video/qu1YjKVJ00E/w-d-xo.html
I have a skepticism about the betweenness centrality in this lecture cause the CB(3) in undirected condition would be 6 but you mentioned it as 0.6. I hope you will mend it.
Wouldn't the undirected graph be /7 and a directed graph be /10. Given the undirected edges would be counted up to 7 and for it to be 10 it'll have to include the directional aspect
Can you explain the 10 path in the betweenness centrality of node 3. thanks
Node 3 can be in a maximum of 10 shortest paths (Assuming the graph to be undirected) i.e., the shortest path of all possible pairs except for Node 3 which are,
1. Node 1 and Node 2
2. Node 1 and Node 4
3. Node 1 and Node 5
4. Node 1 and Node 6
5. Node 2 and Node 4
6. Node 2 and Node 5
7. Node 2 and Node 6
8. Node 4 and Node 5
9. Node 4 and Node 6
10. Node 5 and Node 6.
If Node 3 happens to be in all of them, it will attain the highest betweenness centrality of 1. If it is in none of the shortest paths then it will get the lowest value of 0 or otherwise, anywhere between the two.
@@computationallinguistics3734 Thank you, I got it.
Thankyou i was confused here @@computationallinguistics3734
precious concepts love you sir💖💖💖💖💖💖💖💖💖💖💖💖💖💖💖💖
can you name an example for a network, where betweenness would be a good fit (or even a better fit than closeness / degree) to measure the importance of a node in the network?
Betweenness centrality offers a more in-depth centrality or importance of a node. A node with more connections will have high degree centrality like a hub in a community. This is irrespective of the fact that those nodes may be of least importance with only few other connections. So, it is computationally efficient but not very reliable. Closeness centrality of the nodes that may have fewer connections but their connections link them to diverse communities. These nodes are comparatively more important as they get information from different communities. There can be multiple nodes with high closeness centrality. It offers more information in comparison to degree centrality but is computationally more expensive as well. Betweenness centrality of a node is the special kind of closeness centrality where the node acts as the only door-way to a different community. Such nodes are absolutely vital as losing them would shutdown the inflow of information from one or more communities. So, they not only link multiple communities but are often the only source to them. Again, more information but computationally more expensive. This is a very useful resource on the topic:
www.sciencedirect.com/science/article/pii/B9780128016565000214
@@computationallinguistics3734 thank you very much for this quick and in-depth answer!
how there is 6 in numarator for node 3
Good video, however you should have a double ended arrow between 3 and 4. Thank you
actually really useful, thanks!
Glad to hear!
Really helpful!! thanks
not understtood
Too fast explanation. Nothing understood.
Sorry to hear that, I will do another video on it.
@@computationallinguistics3734 you havent
@@yagzyalcntas553 I have just made one now. Thanks for reminding me. th-cam.com/video/qu1YjKVJ00E/w-d-xo.html
@@computationallinguistics3734 well my final was this morning but thanks anyway :)
@@yagzyalcntas553 hope you have done well there
Slow down man. You skipped a lot.
I have made another video on the topic with more detailed evaluation th-cam.com/video/qu1YjKVJ00E/w-d-xo.html