---
res:
  bibo_abstract:
  - Graph neural networks (GNNs) have been successfully applied in many structured
    data domains, with applications ranging from molecular property prediction to
    the analysis of social networks. Motivated by the broad applicability of GNNs,
    we propose the family of so-called RankGNNs, a combination of neural Learning
    to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences
    between graphs, suggesting that one of them is preferred over the other. One practical
    application of this problem is drug screening, where an expert wants to find the
    most promising molecules in a large collection of drug candidates. We empirically
    demonstrate that our proposed pair-wise RankGNN approach either significantly
    outperforms or at least matches the ranking performance of the naive point-wise
    baseline approach, in which the LtR problem is solved via GNN-based graph regression.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Clemens
      foaf_name: Damke, Clemens
      foaf_surname: Damke
      foaf_workInfoHomepage: http://www.librecat.org/personId=48192
    orcid: 0000-0002-0455-0048
  - foaf_Person:
      foaf_givenName: Eyke
      foaf_name: Hüllermeier, Eyke
      foaf_surname: Hüllermeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=48129
  bibo_doi: 10.1007/978-3-030-88942-5
  bibo_volume: 12986
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0302-9743
  - http://id.crossref.org/issn/1611-3349
  - http://id.crossref.org/issn/9783030889418
  - http://id.crossref.org/issn/9783030889425
  dct_language: eng
  dct_publisher: Springer@
  dct_subject:
  - Graph-structured data
  - Graph neural networks
  - Preference learning
  - Learning to rank
  dct_title: Ranking Structured Objects with Graph Neural Networks@
...