SOCIAL NETWORK

Social Network

Social Network

The subject social network

Reducing social relations to a graph (ie, a set of nodes with explicit relationships between them) allows a series of studies, from which conclusions can be drawn from simple (how many per- intermediate persons would be needed to get the mobile number of Beckham) to complex (who is the agent with more influence within a social network) (Ryan, 2011). The first thing to do to analyze this network is to express it as a contact matrix, having as rows and columns actors or agents of this social network.

But networks are made, not born, and depending on how you go grow, the network type and its properties will be different. Therefore, there have proposed different models of network growth. The oldest is that of Erdos-Renyi, a growth model of random networks, which every time you added a new node, linking to a random one. As models so, not bad, and has interesting properties, but there are few networks in the real world to behave well, there is always more cool than other nodes (Boyd, Danah and Ellison, 2008). Networks that result from these two models differ at least as is called the giant component, ie a group of linked nodes between if that group and most of the network nodes. It also produces a percolation effect: there comes a point at which the different components isolated bind. However, in networks with preferential binding the giant component appears with far fewer nodes, because the nodes are connected with more incoming probability to nodes that already have many connections, which obviously will be connected to the rest of the component.

Sociogram

The sociogram, technical instrument emanating from graph theory and is applied in network theory, is shown here as a work item in interventions and research participants, being itself a ...