Hypothesising and problem finding

Visualising relationships could afford administrators/ data analysts

- who to target promotion to?

- retrospection of the effects of a particular promotion e.g. if Creative Edinburgh is trying to promote sculpturing in Scotland. They can see how the network changes before and after the promotion. See how affective the promotion is the what the rate of conversion is.

- another advantage for visualisation is that you can "see" the network structure. How does the network look like apart from the statistical measures, connectivity (sparsely connected or are the connections dense?) apart from knowing the network's influencers centrality and their position in the network. In a snapshot the user sees clearly the network shape reduces the user's cognitive load. (eyes beat memory).

Influence Targeting

So where is this all going?  It’s hard to tell, but if I had to guess I would say that we’ll end up with an approach that looks a lot like the marketing tactics of the past.
Generally speaking, we target not because we are sure that we have the right people, but we want to exclude those that are least likely to be valuable.

Influencer Marketing, as increasingly practised in a commercial context, comprises four main activities:

• Identifying influencers, and ranking them in order of importance.
• Marketing to influencers, to increase awareness of the firm within the influencer community
• Marketing through influencers, using influencers to increase market awareness of the firm amongst target markets
• Marketing with influencers, turning influencers into advocates of the firm.

Showing network properties ??
Degree Centrality:  How many links each person of interest had to the rest of the network.
Betweenness Centrality:  Their location in the network relative to other members.
Closeness Centrality:  The average social distance between a particular member and all of
the other members of the network.

User fitness model?
In complex network theory, the fitness model is a model of the evolution of a network: how the links between nodes change over time depends on the fitness of nodes. Fitter nodes attract more links at the expense of less fit nodes.
It has been used to model the network structure of the World Wide Web.