Network link prediction based on machine learning methods

Paul Chan

doi:10.1117/12.2615308

15 October 2021 Network link prediction based on machine learning methods

Paul Chan

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330D (2021) https://doi.org/10.1117/12.2615308
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

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.

Citation Download Citation

Paul Chan "Network link prediction based on machine learning methods", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330D (15 October 2021); https://doi.org/10.1117/12.2615308

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