@inproceedings{27381,
  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.}},
  author       = {{Damke, Clemens and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of The 24th International Conference on Discovery Science (DS 2021)}},
  editor       = {{Soares, Carlos and Torgo, Luis}},
  isbn         = {{9783030889418}},
  issn         = {{0302-9743}},
  keywords     = {{Graph-structured data, Graph neural networks, Preference learning, Learning to rank}},
  location     = {{Halifax, Canada}},
  pages        = {{166--180}},
  publisher    = {{Springer}},
  title        = {{{Ranking Structured Objects with Graph Neural Networks}}},
  doi          = {{10.1007/978-3-030-88942-5}},
  volume       = {{12986}},
  year         = {{2021}},
}

@inproceedings{19953,
  abstract     = {{Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.}},
  author       = {{Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}},
  editor       = {{Jialin Pan, Sinno and Sugiyama, Masashi}},
  keywords     = {{graph neural networks, Weisfeiler-Lehman test, cycle detection}},
  location     = {{Bangkok, Thailand}},
  pages        = {{49--64}},
  publisher    = {{PMLR}},
  title        = {{{A Novel Higher-order Weisfeiler-Lehman Graph Convolution}}},
  volume       = {{129}},
  year         = {{2020}},
}