Predicting the Evolution of a node’s location in a network
Researchers at University of Notre Dame developed the Node Prominence Profile methodology to accurately predict the evolution of a node’s centrality in a social network. The article describes how NPP reconciles the two important principles that determine a node’s location: preferential attachment and triadic closure. By applying the Strong Triadic Closure Property to a set of nodes, one can predict that if node A has a strong relationship with both B and C, then it is highly likely that B and C will form some sort of relationship as well. This abstract principle applied to the social networks described in the paper could be of great use. NPP could be revolutionary for examining and predicting activity of a number of important social networks, such as in financial markets and military operations. An example that the article provides is identifying individuals on the fringe of a network that will rise to become central in an adversary’s network, like a potential terrorist leader.
The researchers who developed NPP used the Strong Triadic Closure Property that we learned about in class to create a way to make predictions about the evolution of a network. We have so far examined networks in a single state of being, but it is interesting to imagine adding a dynamic quality to a network, which is the reality of a social network where relationships are constantly being created and destroyed. I’m thinking that a social media site such as Facebook or Twitter might use NPP to suggest potential friends to a user by examining their current connections and predicting accordingly.
https://phys.org/news/2015-02-paper-focuses-degree-centrality-networks.html