Potential Energy to Improve Link Prediction With Relational Graph Neural Networks

Simone Colombo, Dimitrios Alivanistos, Michael Cochez. Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. In Andreas Martin 0001, Knut Hinkelmann, Hans-Georg Fill, Aurona Gerber, Doug Lenat, Reinhard Stolle, Frank van Harmelen, editors, Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022. Volume 3121 of CEUR Workshop Proceedings, CEUR-WS.org, 2022. [doi]

@inproceedings{ColomboAC22,
  title = {Potential Energy to Improve Link Prediction With Relational Graph Neural Networks},
  author = {Simone Colombo and Dimitrios Alivanistos and Michael Cochez},
  year = {2022},
  url = {http://ceur-ws.org/Vol-3121/short2.pdf},
  researchr = {https://researchr.org/publication/ColomboAC22},
  cites = {0},
  citedby = {0},
  booktitle = {Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022},
  editor = {Andreas Martin 0001 and Knut Hinkelmann and Hans-Georg Fill and Aurona Gerber and Doug Lenat and Reinhard Stolle and Frank van Harmelen},
  volume = {3121},
  series = {CEUR Workshop Proceedings},
  publisher = {CEUR-WS.org},
}