Link-Prediction

Link prediction is to predict the existence of a link between two nodes in a network.

Dataset

The training network is a partial crawl of the Twitter social network from several years ago. The nodes in the network—Twitter users—have been given randomly assigned IDs, and a directed edge from node A to B represents that user A follows B. The training network is a subgraph of the entire network. Starting from several random seed nodes, data maker proceeded to obtain the friends of the seeds, then their friends’ friends, and so on for several iterations.

The test data is a list of 2,000 edges, and the task is to predict if each of those test edges are really edges in the Twitter network or are fake ones. 1,000 of these test edges are real and withheld from the training network, while the other 1,000 do not actually exist

How to run?

1. Random Sampling

   python sampling.py

2. Generate Directed Features

   python directed_features.py

3. Generate Undirected Features

   python undirected_features.py

4. Generate Experimental Features

   python experimental_features.py

5. Logistic Regression

   run LogisticRegression.ipynb

6. LightGBM

   run LightGBM.ipynb

7. Deep Learning

   run DeepLearning.ipynb