---
_id: '27381'
abstract:
- lang: eng
  text: 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:
- first_name: Clemens
  full_name: Damke, Clemens
  id: '48192'
  last_name: Damke
  orcid: 0000-0002-0455-0048
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks.
    In: Soares C, Torgo L, eds. <i>Proceedings of The 24th International Conference
    on Discovery Science (DS 2021)</i>. Vol 12986. Lecture Notes in Computer Science.
    Springer; 2021:166-180. doi:<a href="https://doi.org/10.1007/978-3-030-88942-5">10.1007/978-3-030-88942-5</a>'
  apa: Damke, C., &#38; Hüllermeier, E. (2021). Ranking Structured Objects with Graph
    Neural Networks. In C. Soares &#38; L. Torgo (Eds.), <i>Proceedings of The 24th
    International Conference on Discovery Science (DS 2021)</i> (Vol. 12986, pp. 166–180).
    Springer. <a href="https://doi.org/10.1007/978-3-030-88942-5">https://doi.org/10.1007/978-3-030-88942-5</a>
  bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer
    Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986},
    DOI={<a href="https://doi.org/10.1007/978-3-030-88942-5">10.1007/978-3-030-88942-5</a>},
    booktitle={Proceedings of The 24th International Conference on Discovery Science
    (DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke},
    editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture
    Notes in Computer Science} }'
  chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with
    Graph Neural Networks.” In <i>Proceedings of The 24th International Conference
    on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, 12986:166–80.
    Lecture Notes in Computer Science. Springer, 2021. <a href="https://doi.org/10.1007/978-3-030-88942-5">https://doi.org/10.1007/978-3-030-88942-5</a>.
  ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural
    Networks,” in <i>Proceedings of The 24th International Conference on Discovery
    Science (DS 2021)</i>, Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: <a
    href="https://doi.org/10.1007/978-3-030-88942-5">10.1007/978-3-030-88942-5</a>.'
  mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph
    Neural Networks.” <i>Proceedings of The 24th International Conference on Discovery
    Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, vol. 12986, Springer,
    2021, pp. 166–80, doi:<a href="https://doi.org/10.1007/978-3-030-88942-5">10.1007/978-3-030-88942-5</a>.
  short: 'C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of
    The 24th International Conference on Discovery Science (DS 2021), Springer, 2021,
    pp. 166–180.'
conference:
  end_date: 2021-10-13
  location: Halifax, Canada
  name: 24th International Conference on Discovery Science
  start_date: 2021-10-11
date_created: 2021-11-11T14:15:18Z
date_updated: 2022-04-11T22:08:12Z
department:
- _id: '355'
doi: 10.1007/978-3-030-88942-5
editor:
- first_name: Carlos
  full_name: Soares, Carlos
  last_name: Soares
- first_name: Luis
  full_name: Torgo, Luis
  last_name: Torgo
external_id:
  arxiv:
  - '2104.08869'
intvolume: '     12986'
keyword:
- Graph-structured data
- Graph neural networks
- Preference learning
- Learning to rank
language:
- iso: eng
page: 166-180
publication: Proceedings of The 24th International Conference on Discovery Science
  (DS 2021)
publication_identifier:
  isbn:
  - '9783030889418'
  - '9783030889425'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Ranking Structured Objects with Graph Neural Networks
type: conference
user_id: '48192'
volume: 12986
year: '2021'
...
---
_id: '61025'
abstract:
- lang: eng
  text: The concept of social dominance has been used in a plethora of studies to
    assess animal behaviour and relationships between individuals for nearly a century.
    Nevertheless, a standard approach does not yet exist to assess dominance in species
    that have a nonlinear or weakly linear hierarchical structure. We amassed 316
    published data sets and show that 73.7% of the data sets and 90.3% of 103 species
    that we reviewed do not have a strongly linear structure. Herein, we present a
    novel method, ADAGIO, for assessing the structure of dominance networks. ADAGIO
    computes dominance hierarchies, in the form of directed acyclic graphs, to represent
    the dominance relations of a given group of animals. Thus far, most methods for
    computing dominance ranks assume implicitly that the dominance relation is a total
    order of the individuals in a group. ADAGIO does not assume or require this to
    be always true, and is hence more appropriate for analysing dominance hierarchies
    that are not strongly linear. We evaluated our approach against other frequently
    used methods, I&SI, David's score and Elo-rating, on 12 000 simulated data sets
    and on 279 interaction matrices from published, empirical data. The results from
    the simulated data show that ADAGIO achieves a significantly smaller error, and
    hence performs better when assigning ranks than other methods. Additionally, ADAGIO
    generated accurate dominance hierarchies for empirical data sets with a high index
    of linearity. Hence, our findings suggest that ADAGIO is currently the most reliable
    method to assess social dominance in gregarious animals living in groups of any
    size. Furthermore, since ADAGIO was designed to be generic, its applicability
    has the potential to extend beyond dominance data. The source code of our algorithm
    and all simulations used for this paper are publicly available at http://ngonga.github.io/adagio/.
article_type: original
author:
- first_name: Pamela Heidi
  full_name: Douglas, Pamela Heidi
  id: '72311'
  last_name: Douglas
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
- first_name: Gottfried
  full_name: Hohmann, Gottfried
  last_name: Hohmann
citation:
  ama: Douglas PH, Ngonga Ngomo A-C, Hohmann G. A novel approach for dominance assessment
    in gregarious species: ADAGIO. <i>Animal Behaviour</i>. 2016;123:21-32. doi:<a
    href="https://doi.org/10.1016/j.anbehav.2016.10.014">10.1016/j.anbehav.2016.10.014</a>
  apa: Douglas, P. H., Ngonga Ngomo, A.-C., &#38; Hohmann, G. (2016). A novel approach
    for dominance assessment in gregarious species: ADAGIO. <i>Animal Behaviour</i>,
    <i>123</i>, 21–32. <a href="https://doi.org/10.1016/j.anbehav.2016.10.014">https://doi.org/10.1016/j.anbehav.2016.10.014</a>
  bibtex: '@article{Douglas_Ngonga Ngomo_Hohmann_2016, title={A novel approach for
    dominance assessment in gregarious species: ADAGIO}, volume={123}, DOI={<a href="https://doi.org/10.1016/j.anbehav.2016.10.014">10.1016/j.anbehav.2016.10.014</a>},
    journal={Animal Behaviour}, publisher={Elsevier BV}, author={Douglas, Pamela Heidi
    and Ngonga Ngomo, Axel-Cyrille and Hohmann, Gottfried}, year={2016}, pages={21–32}
    }'
  chicago: 'Douglas, Pamela Heidi, Axel-Cyrille Ngonga Ngomo, and Gottfried Hohmann.
    “A Novel Approach for Dominance Assessment in Gregarious Species: ADAGIO.” <i>Animal
    Behaviour</i> 123 (2016): 21–32. <a href="https://doi.org/10.1016/j.anbehav.2016.10.014">https://doi.org/10.1016/j.anbehav.2016.10.014</a>.'
  ieee: 'P. H. Douglas, A.-C. Ngonga Ngomo, and G. Hohmann, “A novel approach for
    dominance assessment in gregarious species: ADAGIO,” <i>Animal Behaviour</i>,
    vol. 123, pp. 21–32, 2016, doi: <a href="https://doi.org/10.1016/j.anbehav.2016.10.014">10.1016/j.anbehav.2016.10.014</a>.'
  mla: Douglas, Pamela Heidi, et al. “A Novel Approach for Dominance Assessment in
    Gregarious Species: ADAGIO.” <i>Animal Behaviour</i>, vol. 123, Elsevier BV, 2016,
    pp. 21–32, doi:<a href="https://doi.org/10.1016/j.anbehav.2016.10.014">10.1016/j.anbehav.2016.10.014</a>.
  short: P.H. Douglas, A.-C. Ngonga Ngomo, G. Hohmann, Animal Behaviour 123 (2016)
    21–32.
date_created: 2025-08-26T19:24:18Z
date_updated: 2025-08-26T19:57:38Z
department:
- _id: '40'
doi: 10.1016/j.anbehav.2016.10.014
extern: '1'
intvolume: '       123'
keyword:
- aggression
- behaviour
- comparability
- directed acyclic graph
- hierarchy
- linearity
- nonlinearity
- social rank
- totality
language:
- iso: eng
page: 21-32
publication: Animal Behaviour
publication_identifier:
  issn:
  - 0003-3472
publication_status: published
publisher: Elsevier BV
status: public
title: A novel approach for dominance assessment in gregarious species: ADAGIO
type: journal_article
user_id: '72311'
volume: 123
year: '2016'
...
---
_id: '3033'
author:
- first_name: Johannes
  full_name: Blömer, Johannes
  id: '23'
  last_name: Blömer
- first_name: Richard
  full_name: Karp, Richard
  last_name: Karp
- first_name: Emo
  full_name: Welzl, Emo
  last_name: Welzl
citation:
  ama: Blömer J, Karp R, Welzl E. The rank of sparse random matrices over finite fields.
    <i>Random Structures \&#38; Algorithms</i>. 1997;(4):407-419. doi:<a href="https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y">10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y</a>
  apa: Blömer, J., Karp, R., &#38; Welzl, E. (1997). The rank of sparse random matrices
    over finite fields. <i>Random Structures \&#38; Algorithms</i>, (4), 407–419.
    <a href="https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y">https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y</a>
  bibtex: '@article{Blömer_Karp_Welzl_1997, title={The rank of sparse random matrices
    over finite fields}, DOI={<a href="https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y">10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y</a>},
    number={4}, journal={Random Structures \&#38; Algorithms}, author={Blömer, Johannes
    and Karp, Richard and Welzl, Emo}, year={1997}, pages={407–419} }'
  chicago: 'Blömer, Johannes, Richard Karp, and Emo Welzl. “The Rank of Sparse Random
    Matrices over Finite Fields.” <i>Random Structures \&#38; Algorithms</i>, no.
    4 (1997): 407–19. <a href="https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y">https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y</a>.'
  ieee: J. Blömer, R. Karp, and E. Welzl, “The rank of sparse random matrices over
    finite fields,” <i>Random Structures \&#38; Algorithms</i>, no. 4, pp. 407–419,
    1997.
  mla: Blömer, Johannes, et al. “The Rank of Sparse Random Matrices over Finite Fields.”
    <i>Random Structures \&#38; Algorithms</i>, no. 4, 1997, pp. 407–19, doi:<a href="https://doi.org/10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y">10.1002/(SICI)1098-2418(199707)10:4&#60;407::AID-RSA1&#62;3.0.CO;2-Y</a>.
  short: J. Blömer, R. Karp, E. Welzl, Random Structures \&#38; Algorithms (1997)
    407–419.
date_created: 2018-06-05T08:34:49Z
date_updated: 2022-01-06T06:58:52Z
department:
- _id: '64'
doi: 10.1002/(SICI)1098-2418(199707)10:4<407::AID-RSA1>3.0.CO;2-Y
extern: '1'
issue: '4'
keyword:
- random matrices
- rank
- finite fields
page: 407-419
publication: Random Structures \& Algorithms
publication_status: published
status: public
title: The rank of sparse random matrices over finite fields
type: journal_article
user_id: '25078'
year: '1997'
...