@inproceedings{vargas-vieyra-etal-2020-joint,
    title = "Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning",
    author = "Vargas-Vieyra, Mariana  and
      Bellet, Aur{\'e}lien  and
      Denis, Pascal",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Panchenko, Alexander  and
      Malliaros, Fragkiskos D.  and
      Hulpuș, Ioana  and
      Jansen, Peter  and
      Jana, Abhik",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.textgraphs-1.4/",
    doi = "10.18653/v1/2020.textgraphs-1.4",
    pages = "35--45",
    abstract = "Graph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available. These methods typically use a heuristic strategy to construct the graph based on some fixed data representation, independently of the available labels. In this pa- per, we propose to jointly learn a data representation and a graph from both labeled and unlabeled data such that (i) the learned representation indirectly encodes the label information injected into the graph, and (ii) the graph provides a smooth topology with respect to the transformed data. Plugging the resulting graph and representation into existing graph-based semi-supervised learn- ing algorithms like label spreading and graph convolutional networks, we show that our approach outperforms standard graph construction methods on both synthetic data and real datasets."
}