Having looked at the bottom up approaches to finding sub-networks in a graph, I will now read the top down approaches from the Hanneman online text. The bottom up approaches help us understand the processes by which actors build networks. On the other hand, the top down approach helps us find holes, vulnerabilities, or weak spots that define lines of division in a larger group. These help to describe the levels of group selection and the constraints under which actors build networks.
We look at components first in this methodology. Components are subgraphs that have high cohesion and less adhesion.Components can have weak ties whether the direction of the ties don't matter and strong ties which are directed. If we have a metric that establishes connections between components as one where the actors participated in something common, then for a very high threshold, we may have few or no independent actors. and for a lower threshold, we may group them into a component. This therefore is too strong a definition to find weak points.
Blocks and Cutpoints are alternative approaches to finding key weak spots in graphs. If a node is removed and the structure is divided into disjoint parts then that node is called a cutpoint and forms a broker among otherwise disconnected groups. These groups are called blocks. We can find the blocks by the cut points While component analysis looks at missing links, this bi-component analysis looks at vulnerable links.
Lambda sets and bridges is another alternative approach for the same. This ranks each of the relationships in the network in terms of the importance by evaluating how much of the flow goes through each link and when disconnected would greatly disrupt the flow between nodes.
Factions are ideal groupings where the members are closely tied to one another but not to anybody else. This concept helps us assess the degree of factionalization in the population.
With the bottom up and top down approaches, we find sub-networks in a graph. The groupings or cliques - their number, size and connections can tell us about the behavior of the network as a whole.
We look at components first in this methodology. Components are subgraphs that have high cohesion and less adhesion.Components can have weak ties whether the direction of the ties don't matter and strong ties which are directed. If we have a metric that establishes connections between components as one where the actors participated in something common, then for a very high threshold, we may have few or no independent actors. and for a lower threshold, we may group them into a component. This therefore is too strong a definition to find weak points.
Blocks and Cutpoints are alternative approaches to finding key weak spots in graphs. If a node is removed and the structure is divided into disjoint parts then that node is called a cutpoint and forms a broker among otherwise disconnected groups. These groups are called blocks. We can find the blocks by the cut points While component analysis looks at missing links, this bi-component analysis looks at vulnerable links.
Lambda sets and bridges is another alternative approach for the same. This ranks each of the relationships in the network in terms of the importance by evaluating how much of the flow goes through each link and when disconnected would greatly disrupt the flow between nodes.
Factions are ideal groupings where the members are closely tied to one another but not to anybody else. This concept helps us assess the degree of factionalization in the population.
With the bottom up and top down approaches, we find sub-networks in a graph. The groupings or cliques - their number, size and connections can tell us about the behavior of the network as a whole.
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