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dc.contributor.authorSong, Y
dc.contributor.authorZheng, S
dc.contributor.authorNiu, Z
dc.contributor.authorFu, ZH
dc.contributor.authorLu, Y
dc.contributor.authorYang, Y
dc.date.accessioned2021-01-29T03:25:20Z
dc.date.available2021-01-29T03:25:20Z
dc.date.issued2020
dc.identifier.isbn9780999241165en_US
dc.identifier.issn1045-0823en_US
dc.identifier.doi10.24963/ijcai.2020/392en_US
dc.identifier.urihttp://hdl.handle.net/10072/401561
dc.description.abstractConstructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction and drug design. Graph neural networks, especially message passing neural network (MPNN) and its variants, have recently made remarkable achievements in molecular graph modeling. Albeit powerful, the one-sided focuses on atom (node) or bond (edge) information of existing MPNN methods lead to the insufficient representations of the attributed molecular graphs. Herein, we propose a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by strengthening the message interactions between nodes and edges through a communicative kernel. In addition, the message generation process is enriched by introducing a new message booster module. Extensive experiments demonstrated that the proposed model obtained superior performances against state-of-the-art baselines on six chemical property datasets. Further visualization also showed better representation capacity of our model.en_US
dc.description.peerreviewedYesen_US
dc.publisherInternational Joint Conferences on Artificial Intelligence Organizationen_US
dc.relation.ispartofconferencename29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI2020)en_US
dc.relation.ispartofconferencetitleProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligenceen_US
dc.relation.ispartofdatefrom2021-01-07
dc.relation.ispartofdateto2021-01-15
dc.relation.ispartoflocationYokohama, Japanen_US
dc.relation.ispartofpagefrom2831en_US
dc.relation.ispartofpageto2838en_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processingen_US
dc.subject.fieldofresearchcode0801en_US
dc.titleCommunicative representation learning on attributed molecular graphsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conferencesen_US
dcterms.bibliographicCitationSong, Y; Zheng, S; Niu, Z; Fu, ZH; Lu, Y; Yang, Y, Communicative representation learning on attributed molecular graphs, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 2831-2838en_US
dc.date.updated2021-01-29T03:22:46Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© 2021 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version.en_US
gro.hasfulltextFull Text
gro.griffith.authorYang, Yuedong


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