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 naïve point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, S.: Learning to rank on graphs. Mach. Learn. 81(3), 333–357 (2010)
Bouritsas, G., Frasca, F., Zafeiriou, S., Bronstein, M.M.: Improving graph neural network expressivity via subgraph isomorphism counting (2020)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs (2013)
Burges, C.: From RankNet to LambdaRank to LambdaMART: an overview. Technical report, MSR-TR-2010-82, Microsoft Research (2010)
Burges, C., et al.: Learning to rank using gradient descent. In: ICML (2005)
Cai, J., Fürer, M., Immerman, N.: An optimal lower bound on the number of variables for graph identification. Combinatorica 12(4), 389–410 (1992)
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning. ACM Press (2007)
Damke, C., Melnikov, V., Hüllermeier, E.: A novel higher-order Weisfeiler-Lehman graph convolution. In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Proceedings of Machine Learning Research, vol. 129. PMLR (2020)
Fürer, M.: On the combinatorial power of the Weisfeiler-Lehman algorithm. In: Fotakis, D., Pagourtzis, A., Paschos, V.T. (eds.) CIAC 2017. LNCS, vol. 10236, pp. 260–271. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57586-5_22
Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data (2015)
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., et al.: Open graph benchmark: datasets for machine learning on graphs (2020)
Huber, J., Payne, J.W., Puto, C.: Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. J. Consum. Res. 9(1), 90 (1982)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002. ACM Press (2002)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Köppel, M., Segner, A., Wagener, M., Pensel, L., Karwath, A., Kramer, S.: Pairwise learning to rank by neural networks revisited: reconstruction, theoretical analysis and practical performance. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11908, pp. 237–252. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46133-1_15
Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: ICML, pp. 6661–6670 (2019)
Maron, H., Ben-Hamu, H., Serviansky, H., Lipman, Y.: Provably powerful graph networks. In: NeurIPS 2019, pp. 2153–2164 (2019)
Mesaoudi-Paul, A.E., Hüllermeier, E., Busa-Fekete, R.: Ranking distributions based on noisy sorting. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, pp. 3469–3477 (2018)
Morris, C., Kriege, N.M., Bause, F., Kersting, K., Mutzel, P., Neumann, M.: TUDataset: a collection of benchmark datasets for learning with graphs (2020)
Pfannschmidt, K., Gupta, P., Hüllermeier, E.: Deep architectures for learning context-dependent ranking functions (March 2018)
Rigutini, L., Papini, T., Maggini, M., Scarselli, F.: SortNet: learning to rank by a neural preference function. IEEE Trans. Neural Netw. 22(9), 1368–1380 (2011)
Sculley, D.: Combined regression and ranking. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 25–28 July 2010, pp. 979–988 (2010)
Sterling, T., Irwin, J.J.: ZINC 15 – Ligand discovery for everyone. J. Chem. Inf. Model. 55(11), 2324–2337 (2015)
Vishwanathan, S., Schraudolph, N., Kondor, R., Borgwardt, K.: Graph kernels. J. Mach. Learn. Res. 11, 1201–1242 (2010)
Waegeman, W., Baets, B.D., Boullart, L.: Kernel-based learning methods for preference aggregation. 4OR 7, 169–189 (2009)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2019)
Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: 32nd AAAI Conference on Artificial Intelligence (2018)
Zhang, W., et al.: When drug discovery meets web search: learning to rank for Ligand-based virtual screening. J. Cheminf. 7(1), 1–13 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Damke, C., Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-88942-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88941-8
Online ISBN: 978-3-030-88942-5
eBook Packages: Computer ScienceComputer Science (R0)