### Abstract

Node classification algorithms are widely used for the task of node label prediction in partially labeled graph data. In many problems, a user may wish to associate a confidence level with a prediction such that the error in the prediction is guaranteed. We propose adopting the Conformal Prediction framework [17] to obtain guaranteed error bounds in node classification problem. We show how this framework can be applied to (1) obtain predictions with guaranteed error bounds, and (2) improve the accuracy of the prediction algorithms. Our experimental results show that the Conformal Prediction framework can provide up to a 30% improvement in node classification algorithm accuracy while maintaining guaranteed error bounds on predictions.

Original language | English (US) |
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Title of host publication | Complex Networks XI - Proceedings of the 11th Conference on Complex Networks, CompleNet 2020 |

Editors | Hugo Barbosa, Ronaldo Menezes, Jesus Gomez-Gardenes, Bruno Gonçalves, Giuseppe Mangioni, Marcos Oliveira |

Publisher | Springer |

Pages | 26-38 |

Number of pages | 13 |

ISBN (Print) | 9783030409425 |

DOIs | |

State | Published - Jan 1 2020 |

Event | 11th International Conference on Complex Networks, CompleNet 2020 - Exeter, United Kingdom Duration: Mar 31 2020 → Apr 3 2020 |

### Publication series

Name | Springer Proceedings in Complexity |
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ISSN (Print) | 2213-8684 |

ISSN (Electronic) | 2213-8692 |

### Conference

Conference | 11th International Conference on Complex Networks, CompleNet 2020 |
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Country | United Kingdom |

City | Exeter |

Period | 3/31/20 → 4/3/20 |

### Keywords

- Bounded error rates
- Conformal prediction
- Node classification

### ASJC Scopus subject areas

- Applied Mathematics
- Modeling and Simulation
- Computer Science Applications

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## Cite this

*Complex Networks XI - Proceedings of the 11th Conference on Complex Networks, CompleNet 2020*(pp. 26-38). (Springer Proceedings in Complexity). Springer. https://doi.org/10.1007/978-3-030-40943-2_3