What is Degree Centrality?
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This video explains how and
This video explains how and why different types of degree centrality can be calculated and includes exercises to practice this. The most simple and popular centrality measurement is the degree centrality. What is degree centrality?
DEGREE UNDIRECTED NETWORK
Centrality is a common measure in network analysis, but it’s often poorly understood. The degree centrality counts the number of neighbours of a node. This centrality can be used to find nodes in a social network with many direct friendships and is often considered a measure of activity, e.g. Facebook.
ADJACENCY MATRIX
Another representation of the network is shown in the adjacency matrix. It is easier to calculate scores with a matrix. The degree is now the sum of the values in a row or column.
NETWORK VISUALISATION
The centrality can be indicated by size and colour. These are two visualization techniques to draw attention to which node is important.
📽️ Ways to Visualise a Network 🤔👁️🧐😃: Ways to Visualise a Network
DEGREE NORMALISATION
If we compare networks and a node (n) has a degree of 9, is it important?
This depends, if a high-density network has 11 nodes: yes, it is important: 9 out of 10 potential edges (n-1). But if there are 101 nodes, the node is not important, 9 out of 100 edges.
That's why you need to normalise centrality scores; divide score by the maximum possible edges. If we do not allow a link between a node and itself, this is n - 1. This places all scores in the range of 0 to 1. A node with a normalised degree of 1 is connected to all nodes in the network. Now networks have the same scale and can be compared.
📽️ Normalisation vs standardisation 📏: Normalization vs Standardization
DEGREE DIRECTED NETWORK
In a directed network we have 2 measures of degree centrality:
• in-degree centrality or degree prestige: number of INcoming arrows to a node;
• out-degree centrality: number of OUTgoing arrows to other nodes.
If the arrows represent the communication in a social media network, e.g. Instagram.
DEGREE WEIGHTED NETWORK
You can calculate degree centrality with weighted networks as well. In that case, the sum of weights of all neighbour edges is the degree.
PROS and CONS
👍Compared to other centrality measures, degree centrality is the simplest to calculate.
👎 The degree of a node only measures locally; it doesn't really tell us where the node is in the network. A node with a high degree centrality can be far out from the core of the network, the periphery of a network.
OTHER CENTRALITY MEASURES
Degree centrality is just one of many measures of centrality. Katz, power and eigenvector centrality are extensions of the degree centrality and states that a node is important if its neighbours are important.
The degree centrality is perhaps the most simple and popular, but in most cases, there is another centrality that fits better.
📽️ What is Network Centrality? What is Network Centrality?
ℹ️ 🔗 What is degree centrality? https://symbio6.nl/en/blog/analysis/d...
TIMESTAMPS
0:00 Degree undirected network
0:30 Adjacency matrix
1:04 Visualisation
1:20 Exercise: normalisation
2:06 Degree directed network
2:28 Exercise: calculate
2:58 Degree weighted network
3:08 Pros and cons degree centrality
3:31 Other centrality measures
3:46 Conclusion
VIDEOS IN THIS SERIES 🎞️
📽️ Centrality: Data Set vs Network Centrality: Data Set vs Network
📽️ What is Closeness Centrality? What is Closeness Centrality?
📽️ Closeness vs Harmonic Centrality Closeness vs Harmonic Centrality
📽️ What is Betweenness Centrality? What is Betweenness Centrality?
📽️ Why Network Analysis 🤔🧐: Why Network Analysis?
📽️ How Symbio6 Improves Your Business 🤝📈🤩: How Symbio6 Improves Your Business 🤝📈🤩
Get started with network analysis today! It has never been so easy to improve your business, we are 😀 to help you. https://symbio6.nl/en/
ABOUT US
Symbio6 gets the best out of relational data to help organizations perform even better. We do this by helping to discover, collect, store, analyse and visualize relational data and convert this into concrete actions to 📈 your business. https://symbio6.nl/en/about-us
#nodecentrality #startnetworkanalysistoday #symbio6
CONTRIBUTIONS
Music: Scott Holmes Music - New Beginnings
Vignery, K., & Laurier, W. (2020). A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?. PLoS One, 15(12), e0244377.
DEGREE UNDIRECTED NETWORK
Centrality is a common measure in network analysis, but it’s often poorly understood. The degree centrality counts the number of neighbours of a node. This centrality can be used to find nodes in a social network with many direct friendships and is often considered a measure of activity, e.g. Facebook.
ADJACENCY MATRIX
Another representation of the network is shown in the adjacency matrix. It is easier to calculate scores with a matrix. The degree is now the sum of the values in a row or column.
NETWORK VISUALISATION
The centrality can be indicated by size and colour. These are two visualization techniques to draw attention to which node is important.
📽️ Ways to Visualise a Network 🤔👁️🧐😃: Ways to Visualise a Network
DEGREE NORMALISATION
If we compare networks and a node (n) has a degree of 9, is it important?
This depends, if a high-density network has 11 nodes: yes, it is important: 9 out of 10 potential edges (n-1). But if there are 101 nodes, the node is not important, 9 out of 100 edges.
That's why you need to normalise centrality scores; divide score by the maximum possible edges. If we do not allow a link between a node and itself, this is n - 1. This places all scores in the range of 0 to 1. A node with a normalised degree of 1 is connected to all nodes in the network. Now networks have the same scale and can be compared.
📽️ Normalisation vs standardisation 📏: Normalization vs Standardization
DEGREE DIRECTED NETWORK
In a directed network we have 2 measures of degree centrality:
• in-degree centrality or degree prestige: number of INcoming arrows to a node;
• out-degree centrality: number of OUTgoing arrows to other nodes.
If the arrows represent the communication in a social media network, e.g. Instagram.
DEGREE WEIGHTED NETWORK
You can calculate degree centrality with weighted networks as well. In that case, the sum of weights of all neighbour edges is the degree.
PROS and CONS
👍Compared to other centrality measures, degree centrality is the simplest to calculate.
👎 The degree of a node only measures locally; it doesn't really tell us where the node is in the network. A node with a high degree centrality can be far out from the core of the network, the periphery of a network.
OTHER CENTRALITY MEASURES
Degree centrality is just one of many measures of centrality. Katz, power and eigenvector centrality are extensions of the degree centrality and states that a node is important if its neighbours are important.
The degree centrality is perhaps the most simple and popular, but in most cases, there is another centrality that fits better.
📽️ What is Network Centrality? What is Network Centrality?
ℹ️ 🔗 What is degree centrality? https://symbio6.nl/en/blog/analysis/d...
TIMESTAMPS
0:00 Degree undirected network
0:30 Adjacency matrix
1:04 Visualisation
1:20 Exercise: normalisation
2:06 Degree directed network
2:28 Exercise: calculate
2:58 Degree weighted network
3:08 Pros and cons degree centrality
3:31 Other centrality measures
3:46 Conclusion
VIDEOS IN THIS SERIES 🎞️
📽️ Centrality: Data Set vs Network Centrality: Data Set vs Network
📽️ What is Closeness Centrality? What is Closeness Centrality?
📽️ Closeness vs Harmonic Centrality Closeness vs Harmonic Centrality
📽️ What is Betweenness Centrality? What is Betweenness Centrality?
📽️ Why Network Analysis 🤔🧐: Why Network Analysis?
📽️ How Symbio6 Improves Your Business 🤝📈🤩: How Symbio6 Improves Your Business 🤝📈🤩
Get started with network analysis today! It has never been so easy to improve your business, we are 😀 to help you. https://symbio6.nl/en/
ABOUT US
Symbio6 gets the best out of relational data to help organizations perform even better. We do this by helping to discover, collect, store, analyse and visualize relational data and convert this into concrete actions to 📈 your business. https://symbio6.nl/en/about-us
#nodecentrality #startnetworkanalysistoday #symbio6
CONTRIBUTIONS
Music: Scott Holmes Music - New Beginnings
Vignery, K., & Laurier, W. (2020). A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?. PLoS One, 15(12), e0244377.
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