Evaluating graph neural networks under graph sampling scenarios.

ABSTRACT:

Background

It is often the case that only a portion of the underlying network structure is observed in real-world settings. However, as most network analysis methods are built on a complete network structure, the natural questions to ask are: (a) how well these methods perform with incomplete network structure, (b) which structural observation and network analysis method to choose for a specific task, and (c) is it beneficial to complete the missing structure.

Methods

In this paper, we consider the incomplete network structure as one random sampling instance from a complete graph, and we choose graph neural networks (GNNs), which have achieved promising results on various graph learning tasks, as the representative of network analysis methods. To identify the robustness of G

SUBMITTER: Wei Q 

PROVIDER: S-EPMC9044246 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature