Given a directed social graph, the model has to predict missing links to recommend users (Link Prediction in graph)
Taken data from facebook's recruting challenge on kaggle https://www.kaggle.com/c/FacebookRecruiting
Data contains two columns source and destination. The source and destination columns contain information about the
users and a source, destination pair signifies that they are connected in the network. Here,
a link between the user u1 and u2 denotes a 1-way relationship i.e u1 follows u2
- Data columns (total 2 columns):
- source_node int64
- destination_node int64
Above is the snapshot of the raw data in train.csv file. It only has two columns source_node and destination_node. This is a pure graph based link prediction problem as we have no other meta information
- Generated training samples of good and bad links from given directed graph. 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.
- No low-latency requirement.
- Probability of prediction is useful to recommend highest probability links
- F1 score
- Confusion matrix
Precision: Suggesting connections that are most likely to be correct
Recall: We try and not to miss out any connections
Since we want both precision and recall to be high, we choose F1 score as a KPI along Confusion matrix to summarize
the performance of the model
- https://www.appliedaicourse.com/course/11/Applied-Machine-learning-course
- http://be.amazd.com/link-prediction/
- https://storage.googleapis.com/kaggle-forum-message-attachments/2594/supervised_link_prediction.pdf
- 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