vinitsr7/instagram-followee-prediction

Given a directed social graph, we have to predict missing edges to recommend friends/connnections/followers in the graph.

Social-network-Graph-Link-Prediction---Facebook-Challenge

Problem statement:

Given a directed social graph, we have to predict missing links to recommend friends/connnections/followers (Link Prediction in graph)

Data Overview

Dataset from facebook's recruting challenge on kaggle https://www.kaggle.com/c/FacebookRecruiting
data contains two columns: source and destination edge pairs in the directed graph.
- Data columns (total 2 columns):
- source_node int64
- destination_node int64

Mapping the problem to supervised learning problem:

• Map this to a binary classification task with 0 implying an absence of an edge and 1 implying the presence of the edge.
  Now, we need to featurize a pair of vertices (u_i,u_j) such that these features can help us predict the presence/absence of an edge.

Performance metric for supervised learning:

• Both precision and recall are important, hence F1 score is good choice
• Confusion matrix

Requinments:

• Python3
• numpy
• pandas
• matplotlib
• seaborn
• networkx
  NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. We are using it to implement our Graphs.

How To Use?

1. Run FB_EDA file, which has all the Exploratory Data analysis, and train test split process.
2. Run FB_Featurization which has all featurization done.
3. Run FB_Models, training part is done by Random Forest Classifier

Featurization:

we will create these features for both train and test data points

1. jaccard_followers
2. jaccard_followees
3. cosine_followers
4. cosine_followees
5. num_followers_s
6. num_followees_s
7. num_followers_d
8. num_followees_d
9. inter_followers
10. inter_followees
11. adar index
12. is following back
13. belongs to same weakly connect components
14. shortest path between source and destination
15. Weight Features
    • weight of incoming edges
    • weight of outgoing edges
    • weight of incoming edges + weight of outgoing edges
    • weight of incoming edges * weight of outgoing edges
    • 2*weight of incoming edges + weight of outgoing edges
    • weight of incoming edges + 2*weight of outgoing edges
16. Page Ranking of source
17. Page Ranking of dest
18. katz of source
19. katz of dest
20. hubs of source
21. hubs of dest
22. authorities_s of source
23. authorities_s of dest

Reference to Features:

1. Jaccard Distance: http://www.statisticshowto.com/jaccard-index/
2. Cosine Similarity(Otsuka-Ochiai coefficient): https://en.wikipedia.org/wiki/Cosine_similarity
3. Page Rank
   • https://en.wikipedia.org/wiki/PageRank
4. https://networkx.github.io/documentation/networkx1.10/reference/generated/networkx.algorithms.link_analysis.pagerank_alg.pagerank.html
5. Shortest Path: https://stackoverflow.com/questions/9430027/networkx-shortest-path-length
6. Weakly Connected Components: https://www.quora.com/What-are-strongly-and-weakly-connected-components
7. Adamic/Adar Index: https://en.wikipedia.org/wiki/Adamic/Adar_index
8. Katz Centrality:
   • https://en.wikipedia.org/wiki/Katz_centrality
9. https://www.geeksforgeeks.org/katz-centrality-centrality-measure/
10. HITS Score(Hubs and Authority): https://en.wikipedia.org/wiki/HITS_algorithm

• 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