👫 In this challenge we are given a directed social graph, and we have to predict missing links to recommend users (Link Prediction in graph).

Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)

Our dataset is directed graph data We have approx. 1.86M nodes and 9.43M edges. Data was obtained from kaggle. You can get data from here https://www.kaggle.com/c/FacebookRecruiting We have provided only connected nodes. i.e. 9.43M edges. But for each user among n user's, there is n-1 edges. So, for n nodes total possible edges are of 10^12 order.

• Both precision and recall is important so F1 score is good choice
• Confusion matrix

• If we consider y= 1 , if edge is present in between two nodes.
• We will assume y = 0 , if no edge is present.
• Generated Bad links from graph which are not in graph and whose shortest path is greater than 2

Featurization is the most important part of this case study. Below is the list of extracted features

• Similarity measures
• Jaccard Distance
• Cosine distance
• Ranking Measure
• Page Ranking (https://en.wikipedia.org/wiki/PageRank)
• Graph Features
• Shortest Path
• Checking for same community
• Adamic/Adar Index
• Is following back
• Katz Centrality
• Hits Score
• num followers
• num followees