Learning Label Initialization for Time-Dependent Harmonic Extension.

Amitoz Azad

IJCAI 2022poster1 citations

Learning Label Initialization for Time-Dependent Harmonic Extension.

Abstract

Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version of the Dirichlet problem on graphs and shows how to improve its solution by learning the proper initialization vector on the unlabeled nodes. Further, we show that the improved solution is at par with state-of-the-art methods used for node classification. Finally, we conclude this paper by discussing the importance of parameter t, pros, and future directions.

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BibTeX

@inproceedings{ijcai2022p387,
  title     = {Learning Label Initialization for Time-Dependent Harmonic Extension.},
  author    = {Azad, Amitoz},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {2791--2797},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/387},
  url       = {https://doi.org/10.24963/ijcai.2022/387},
}