Abstract
Network structure is an important and effective tool for describing things and the interrelationships between things and has been widely used in various research areas of complex networks, such as social networks, power networks, and biological networks. However, the observed networks are often incomplete and have missing nodes because of factors, such as data collection cost or permission. Missing nodes detection involves inferring whether there are missing nodes in the network and the location and neighborhood structure of the missing nodes based on the observed part of the complex networks. In this paper, a missing node detection algorithm based on graph convolutional networks is proposed. The algorithm divides the missing nodes detection task of the complex works into two subtasks: missing nodes existence detection and missing nodes structure inference. Specifically, we use graph convolutional networks to extract the nodes feature information and structure information, convert each node in the network into a low-dimensional latent representation, determine the existence of missing nodes by performing two consecutive layers of fully connected operations on latent variables, and perform linear operations on latent variables to infer the connection relationship between the missing nodes and other observable nodes. Then, we detect missing nodes in the complex network. We conduct experiments on real network datasets and artificially generated network datasets and find that the model can well solve the problem of detecting the missing nodes in complex networks and recovering the missing nodes and their connection structures with observable nodes of the networks.
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Acknowledgements
This work was supported by Shanghai Philosophy and Social Science Planning Project (No.2021BTQ003), China Postdoctoral Science Foundation (2021M69235).
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Liu, C., Li, Z. & Zhou, L. Missing nodes detection for complex networks based on graph convolutional networks. J Ambient Intell Human Comput 14, 9145–9158 (2023). https://doi.org/10.1007/s12652-022-04418-3
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DOI: https://doi.org/10.1007/s12652-022-04418-3