Conventions and basic information

Social Network Analysis (SNA) is done to understand the relationships between people, organizations, groups, urls or any other connected information entities. The most intuitive way of representing these relationships is of course done by using a graph. İn such a graph the connected entities would be the nodes and the relations between them are represented as edges.

Common metrics

İn order to understand how well people are connected a few centrality measures are proposed.

Degree Centrality: The number of direct connections a node has.

Betweenness Centrality: The likelihood of one node to be on the most direct route between to other nodes.

Closeness Centrality: The distance from one node to each other in the network.

Eigenvector: The degree to which one node is connected to other well connected nodes.

Due to these measures a network can be categorized into four patterns (Hubs, Bridges, İslands, Crowds/Clusters) depending on its structure. Nowadays it is essential for e.g. companies to understand how well they are connected and how they can reach their target groups. However this is not a trivial problem since it is way more complex than simply counting followers or likes. For example researchers have shown that there is a horizon in networks over which we have usually no influence. They propose, that the key paths are only x ≤ 3 steps away. Therefore the question the evluate the position within a network would be ‘Who can reach the most people with ≤ 3 Steps.’