| Téléchargement | - Voir la version finale : Self-supervised graph-level representation learning with local and global structure (PDF, 1.2 Mio)
- Voir les données supplémentaires : Self-supervised graph-level representation learning with local and global structure (ZIP, 7.0 Mio)
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| Auteur | Rechercher : Xu, Minghao; Rechercher : Wang, Hang; Rechercher : Ni, Bingbing; Rechercher : Guo, Hongyu1; Rechercher : Tang, Jian |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 38th International Conference on Machine Learning (PMLR 2021), July 18-24, 2021 (Held Virtually) |
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| Sujet | neuroscience and cognitive science; human or animal learning; embedding and representation learning |
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| Résumé | This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery. Existing methods mainly focus on preserving the local similarity structure between different graph instances but fail to discover the global semantic structure of the entire data set. In this paper, we propose a unified framework called Local-instance and Global-semantic Learning (GraphLoG) for self-supervised whole-graph representation learning. Specifically, besides preserving the local similarities, GraphLoG introduces the hierarchical prototypes to capture the global semantic clusters. An efficient online expectation-maximization (EM) algorithm is further developed for learning the model. We evaluate GraphLoG by pre-training it on massive unlabeled graphs followed by fine-tuning on downstream tasks. Extensive experiments on both chemical and biological benchmark data sets demonstrate the effectiveness of the proposed approach. |
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| Date de publication | v. 2021-07-24 |
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| Maison d’édition | ML Research Press |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 911e8cdd-1987-492f-8c11-b252c070c26c |
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| Enregistrement créé | 2022-07-18 |
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| Enregistrement modifié | 2022-07-18 |
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