Thanks in a million. Great content. Awesome. Very well explained. I couldn't find this explanation--simply put anywhere else. Great teachers are hard to find. Grade: A++💥
2:09 to normalize, multiply with maximum possible edges or nodes? If it is edges then the calculation is wrong. Please connect me if I understood it incorrectly
Thank you for your question; the video might have presented the concept briefly, leading to some confusion. In the video, the assumption is that we are dealing with an undirected graph. When we refer to "maximum possible edges," we mean the maximum number of edges that a single node can have in this type of graph. In undirected graphs, each node can have a maximum of (number of nodes - 1) edges connecting to it. Alternatively, you can think of it as the "degree" of a node, which represents the number of edges connected to that node. To clarify, you can normalize the centrality scores by multiplying them with either the maximum possible number of nodes minus 1 or the maximum number of edges from a node (its degree). Both approaches achieve the same result in the context of an undirected graph. We hope this explanation helps clarify the concept for you. If you have any further questions, please feel free to ask.
@@symbio6 now I got it and it makes sense. Thank you so much! Your videos are really informative. I went thru all of them and learned a lot. Very simple and easy to understand! 🙏
I'm delighted to hear that you found our videos informative and easy to understand! Thank you for taking the time to watch them all and for your kind words. Your feedback is greatly appreciated!
Thanks very much for the video. You mentioned that many programs calculate out-closeness in directed networks. Is the Gephi one of them? Also, does it use the inverse of the sum of the shortest distances or the inverse of the average of the shortest distances? I could not find a source that clarified these two issues. Is there source you can recommend?
Absolutely, Gephi is indeed one of those programs. This open-source network analysis and visualization tool, supports various centrality measures, including closeness centrality. It offers functionality for analyzing both directed and undirected networks. And there are others too. To compute closeness centrality in directed networks, several software tools and libraries excel in handling such computations efficiently. Here are some of the prominent ones: • NetworkX: Python library for complex networks with closeness_centrality function. • Cytoscape: Popular tool in biological research with Network Analyzer plugin. • igraph: Available in R, Python, and C/C++ for efficient network analysis. • Neo4j: Graph database with Graph Data Science Library for centrality algorithms. • UCINET: Comprehensive software for social network data analysis. In Gephi, the closeness centrality calculation for directed networks typically involves taking the reciprocal of the sum of shortest path distances from a node to all other nodes in the network. This formula prioritizes nodes with shorter total distances, indicating their central position in terms of efficient communication or interaction reach. However, testing with a simple network may be necessary to confirm. The key distinction lies in normalization: the inverse of the sum method emphasizes total distance, while the inverse of the average method normalizes this distance by the number of nodes. Consequently, the latter method scales closeness centrality by the network's size, yielding a measure that's more intuitive and comparable across different contexts. I hope this answers your question. Good luck with your network analyses.
@@symbio6 Thank you very much for the information, it was very helpful. Thank you also for your quick response and interest. Hope you continue to make informative videos. Best wishes.
I’m sorry the music made it hard to focus! Thanks for sticking with it, though. Closeness centrality measures how close a node is to all other nodes in a network, helping to identify the most influential points. I hope the content was still helpful, and I appreciate your feedback!
Bedankt voor je 10, daar zijn we heel blij mee. Je hebt gelijk, maar dat is ingewikkelder te produceren, maar we nemen het mee voor toekomstige videos. Bedankt voor je feedback.
Thanks in a million. Great content. Awesome. Very well explained. I couldn't find this explanation--simply put anywhere else. Great teachers are hard to find. Grade: A++💥
You are most welcome; you can also learn more about other centralities on the channel.
2:09 to normalize, multiply with maximum possible edges or nodes? If it is edges then the calculation is wrong. Please connect me if I understood it incorrectly
Thank you for your question; the video might have presented the concept briefly, leading to some confusion.
In the video, the assumption is that we are dealing with an undirected graph. When we refer to "maximum possible edges," we mean the maximum number of edges that a single node can have in this type of graph. In undirected graphs, each node can have a maximum of (number of nodes - 1) edges connecting to it. Alternatively, you can think of it as the "degree" of a node, which represents the number of edges connected to that node.
To clarify, you can normalize the centrality scores by multiplying them with either the maximum possible number of nodes minus 1 or the maximum number of edges from a node (its degree). Both approaches achieve the same result in the context of an undirected graph.
We hope this explanation helps clarify the concept for you. If you have any further questions, please feel free to ask.
@@symbio6 now I got it and it makes sense. Thank you so much! Your videos are really informative. I went thru all of them and learned a lot. Very simple and easy to understand! 🙏
I'm delighted to hear that you found our videos informative and easy to understand! Thank you for taking the time to watch them all and for your kind words. Your feedback is greatly appreciated!
Thanks very much for the video. You mentioned that many programs calculate out-closeness in directed networks. Is the Gephi one of them? Also, does it use the inverse of the sum of the shortest distances or the inverse of the average of the shortest distances? I could not find a source that clarified these two issues. Is there source you can recommend?
Absolutely, Gephi is indeed one of those programs. This open-source network analysis and visualization tool, supports various centrality measures, including closeness centrality. It offers functionality for analyzing both directed and undirected networks.
And there are others too. To compute closeness centrality in directed networks, several software tools and libraries excel in handling such computations efficiently. Here are some of the prominent ones:
• NetworkX: Python library for complex networks with closeness_centrality function.
• Cytoscape: Popular tool in biological research with Network Analyzer plugin.
• igraph: Available in R, Python, and C/C++ for efficient network analysis.
• Neo4j: Graph database with Graph Data Science Library for centrality algorithms.
• UCINET: Comprehensive software for social network data analysis.
In Gephi, the closeness centrality calculation for directed networks typically involves taking the reciprocal of the sum of shortest path distances from a node to all other nodes in the network. This formula prioritizes nodes with shorter total distances, indicating their central position in terms of efficient communication or interaction reach. However, testing with a simple network may be necessary to confirm.
The key distinction lies in normalization: the inverse of the sum method emphasizes total distance, while the inverse of the average method normalizes this distance by the number of nodes. Consequently, the latter method scales closeness centrality by the network's size, yielding a measure that's more intuitive and comparable across different contexts.
I hope this answers your question. Good luck with your network analyses.
@@symbio6 Thank you very much for the information, it was very helpful. Thank you also for your quick response and interest. Hope you continue to make informative videos. Best wishes.
Thanks for the video!
You're welcome!
music in the video just makes me confused and not able to easily understand.
Anyway thank you
I’m sorry the music made it hard to focus! Thanks for sticking with it, though. Closeness centrality measures how close a node is to all other nodes in a network, helping to identify the most influential points. I hope the content was still helpful, and I appreciate your feedback!
The music on the background is sucks, do not use it again, just an advice.
It is indeed difficult to choose a background music, we will take your advice with us for the next video. Thanks!
The video is a 10 but uses TTS. Find a narrator.
Bedankt voor je 10, daar zijn we heel blij mee. Je hebt gelijk, maar dat is ingewikkelder te produceren, maar we nemen het mee voor toekomstige videos. Bedankt voor je feedback.