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
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
No low-latency requirement.
Probability of prediction is useful to recommend ighest probability links
Both precision and recall is important so F1 score is good choice
Confusion matrix
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