Problem 2 (15 Points Total) Link Prediction on Single Layer Networks Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, and thus we also want to do that. https://github.com/Aghasemian/DptimalLinkPrediction Problem 2 continued on next page. . . 1 Your Name Here Math 76: An Introduction to Network Science (Homework #2)Problem 2 (continued) 1) Finding Useful Data (5 points): Throughout the course we have been talking about research in real life. Please nd three publicly available dataset that belongs to social, biological, and one other category of your choice respectively, and plot them using networkx. Draw the degree distribution and betweenness centrality distribution of the nodes. (Hint: you may nd the following useful https : / / icon. colorado . edu/ #! etworks). 2) Intuition Building (10 points): Use the existing code in the github and readme le, draw the AUC and PR plot for the three networks that you have found. Explain the results and your intuition on why the results make sense to you