Link-Prediction-Using-Graph-Mining

Problem statement:

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

Taken data from facebook's recruting challenge on kaggle https://www.kaggle.com/c/FacebookRecruiting data contains two columns source and destination eac edge in graph

• Data columns (total 2 columns):
• source_node int64
• destination_node int64

Mapping the problem into supervised learning problem:

Generated training samples of good and bad links from given directed graph and for each link got some features like no of followers, is he followed back, page rank, katz score, adar index, some svd fetures of adj matrix, some weight features etc. and trained ml model based on these features to predict link.
Some reference papers and videos :
    https://www.cs.cornell.edu/home/kleinber/link-pred.pdf
    https://www3.nd.edu/~dial/publications/lichtenwalter2010new.pdf
    https://kaggle2.blob.core.windows.net/forum-message-attachments/2594/supervised_link_prediction.pdf
    https://www.youtube.com/watch?v=2M77Hgy17cg

Business objectives and constraints:

No low-latency requirement.
Probability of prediction is useful to recommend ighest probability links

Performance metric for supervised learning:

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

Featurizations:

1] Jaccard Distance
2] Cosine distance
3] Page Ranking
4] Shortest path
5] Checking for same community
6] Adamic/Adar Index
7] Is persion was following back
8] Katz Centrality
9] Hits Score
10] Preferential Attachment
11] SVD features