By Jesse Aragona

  1. As you add years into the network, family connections continue to develop and grow as more are people are born. The major connectors who cause more and more intertwined networks tend to be both the original founders of the network and offspring they inevitably have. As children from different families intermarry, more and more connections develop between groups eventually creating a vast inter connected network especially with the sample data set.
  2. The betweeness centrality is used to find the influence a particular node has over the larger graph, judging it by what connections it helps create where as filtering by degree is simply designed to find the largest amount of connections. While there does tend to be some overlap, those found by filtering by centrality are not necessarily the same as those who have the most connections when filtered by year. While these tools can produce similar results its important to recognize the differences between these two approaches, as commonality doesn’t necessarily produce exact results.
  3. Patterns formed by clustering finds individual who have the same connections, grouping them into a class. They tend to be better represented then those found filtering by year, as well as more closely related. Using this method we can better compare individuals, allowing us to see best who relates to who and how.