A Survey of Network Representation Learning Methods for Link Prediction in Biological Network

(E-pub Abstract Ahead of Print)

Author(s): Jiajie Peng, Guilin Lu, Xuequn Shang*.

Journal Name: Current Pharmaceutical Design


Background: Networks are powerful resources for describing complex systems. Link prediction is an important issue in network analysis and has important practical application value. Network representation learning has proven to be useful for network analysis, especially for link prediction tasks.

Objective: To review the application of network representation learning on link prediction in biological network, we summarize recent methods for link prediction in biological network and discusses the application and significance of network representation learning in link prediction task.

Method & Results: We first introduce the widely used link prediction algorithms, then briefly introduce the development of network representation learning methods, focusing on a few widely used methods, and their application in biological network link prediction. Existing studies demonstrate that using network representation learning to predict link in biological network can achieve better performance. In the end, we discussed some possible future directions.

Keywords: Biological Network, Link Prediction, Network Analysis, Network Representation learning

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Article Details

(E-pub Abstract Ahead of Print)
DOI: 10.2174/1381612826666200116145057
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