@inproceedings{fe54eda6d4b84556a16626736694bc68,
title = "A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning",
abstract = "The task of semi-supervised classification aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds. One of the most popular algorithms relies on the principle of heat diffusion, where the labels of the seeds are spread by thermo-conductance and the temperature of each node at equilibrium is used as a score function for each label. In this paper, we prove that this algorithm is not consistent unless the temperatures of the nodes at equilibrium are centered before scoring. This crucial step does not only make the algorithm provably consistent on a block model but brings significant performance gains on real graphs.",
author = "Thomas Bonald and \{De Lara\}, Nathan",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023 ; Conference date: 28-11-2023 Through 30-11-2023",
year = "2024",
month = jan,
day = "1",
doi = "10.1007/978-3-031-53468-3\_23",
language = "English",
isbn = "9783031534676",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "272--282",
editor = "Hocine Cherifi and Rocha, \{Luis M.\} and Chantal Cherifi and Murat Donduran",
booktitle = "Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications",
}