Having discussed centrality measures in social graph, let us look at some more social Graph techniques.  I'm going to discuss ego network. Ego network has to do with the individual as the focus. But before we delve into that, I will review the idea of embedding in a network. Embedding is the extent to which individuals find themselves in dense strong ties. These ties are reciprocative and they suggest some measure of constraints on individuals from the way that they are connected to others. This gives us an idea of the population and some subtleties but not so much about the positives and negatives facing the individual. If we look at the context of an individual, then we are looking at a local scope and this is the study of ego networks. Ego means an individual focus as in a node of the graph. There are as many egos as there are nodes.  An ego can be a person, group, Or organization. The neighborhood of an ego is the collection of all egos around the individual to which there is a connection or a path. The connections need not be one step but they usually are. The boundaries of an ego network are defined in terms of neighborhood.  Ego networks are generally undirected because the connections are symmetric. If they are different,  then it's possible to define an in network and an out network.  The ties in an in network are inwards and those in the other network are outwards. The strength and weakness of the ties can be defined in probabilities. Using these we can define thresholds for the ones we want to study. Finding these ties is a technique referred to as data extraction.  The second technique is subgraph extraction. The network density of an ego network can be represented by the number of indexes for each ego in a dataset. We will review more graph techniques shortly.
Courtesy: Hanneman online text.