Network analysis can help discover latent information on graph-structured data, and one of the major topics in link
prediction [1]. Given the current state of a graph, the main task of link prediction is to predict the emergence of currently
non-existing associations between nodes [2]. This project handles this problem with a machine learning-based method,
where we compute a series of network parameters, feed them to a simple neural network and obtain the likelihood label
for each non-existing edge. In the topic of connection prediction, this paper focuses on the application effect of mainstream
machine learning algorithms, finds the key statistical parameters in the algorithm, and compares the prediction efficiency
with deep learning. The test data I used came from Facebook. A complex network with 4000 nodes and 88000 links is
constructed as the test object, and the influence of test sample data selection on test results is analyzed.
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