Aline - MO412 A

In a digital activism campaign, hashtags often appear together in posts about different causes. Each hashtag is a node, and an edge between two hashtags means they frequently co-occur. Some hashtags become hubs of influence, connecting many others and shaping which causes gain more visibility. Even algorithms designed to be neutral may reinforce certain hierarchies — after all, Politics is Never Neutral . Consider the imagem below and the following hashtag co-occurrence network: #InnovationForGood (Node 1): k = 4 #SocialJustice (Node 2): k = 3 #ClimateAction (Node 3): k = 3 #HealthForAll (Node 4): k = 2 #EducationMatters (Node 5): k = 1 #GenderRights (Node 6): k = 1 Compute the average nearest neighbor degree k nn for #InnovationForGood (Node 1). Compute the degree assortativity coefficient for the entire network (using the Pearson correlation of degrees). Interpret whether this network shows assortative or disassortative mixing...

You are an analyst for a streaming platform called StreamWorld , which works like a social network: each movie is a node, and connections between movies represent users who watched both. StreamWorld grows following a pattern similar to the Barabási–Albert model : Each new movie connects to m = 3 existing movies. The probability of a movie receiving a new connection is proportional to the number of connections it already has (preferential attachment). At the start ( t = 0 ), there are m₀ = 5 fully connected movies. At time t = 12000 , the network has N = 12000 + m₀ movies, and three movies were released: - Movie A at t A = 100 - Movie B at t B = 1000 - Movie C at t C = 4000 Which of the following is the correct ranking of their expected degrees k i (t) from largest to smallest , what is the approximate ratio between the larges...