It's a very well known fact that we live in an era dominated by online social networks. However, in order to leverage these networks, it would be interesting to know the science behind the success of these networks. An understanding of this science provides a preliminary step towards leveraging these networks for various purposes like advertising, promotions, campaigns etc., And so here I provide you with some interesting properties of networks.
Degree : - It is the number of nodes to which a particular node is connected. In case of a directed graph it can be split into in degree and out degree according to the number of incoming links to and out going links from a particular node.
Centrality
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In general centrality is a measure of the importance of a node. However, to just state that a node is important is vague. There has to be a criterion for the same. Hence we have the following types of centrality
Betweenness Centrality : -It is the number of shortest paths - between every pair of nodes- on which a particular node lies. A high betweenness centrality implies a that a node act as a mediator or bridge between two components of a network. The node "Heather" in the graph below has a high betweenness centrality.
Closeness Centrality : - It is the inverse of the distance from a node to all other nodes. A higher value of this metric indicates a higher closeness of this node to all other nodes in the network. It essentially indicates the importance of a node in terms of its ability to reach all other nodes easily. The largest node in the figure below as a high closeness centrality.
Eigen Vector Centrality : - It is a measure of the influence of the node in a network. It assigns relative scores to nodes on the assumption that nodes closer to important nodes are more important. Google's page rank is a variant of Eigen-vector centrality.
Degree : - It is the number of nodes to which a particular node is connected. In case of a directed graph it can be split into in degree and out degree according to the number of incoming links to and out going links from a particular node.
Centrality
-----------------
In general centrality is a measure of the importance of a node. However, to just state that a node is important is vague. There has to be a criterion for the same. Hence we have the following types of centrality
Betweenness Centrality : -It is the number of shortest paths - between every pair of nodes- on which a particular node lies. A high betweenness centrality implies a that a node act as a mediator or bridge between two components of a network. The node "Heather" in the graph below has a high betweenness centrality.
Closeness Centrality : - It is the inverse of the distance from a node to all other nodes. A higher value of this metric indicates a higher closeness of this node to all other nodes in the network. It essentially indicates the importance of a node in terms of its ability to reach all other nodes easily. The largest node in the figure below as a high closeness centrality.
Eigen Vector Centrality : - It is a measure of the influence of the node in a network. It assigns relative scores to nodes on the assumption that nodes closer to important nodes are more important. Google's page rank is a variant of Eigen-vector centrality.
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