The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Open accessAbstract
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous graph into low-dimensional node representations. Existing models usually define multiple metapaths in a heterogeneous graph to capture the composite relations and guide neighbor selection. However, these models either omit node content features, discard intermediate nodes along the metapath, or only consider one metapath. To address these three limitations, we propose a new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the final performance. Specifically, MAGNN employs three major components, i.e., the node content transformation to encapsulate input node attributes, the intra-metapath aggregation to incorporate intermediate semantic nodes, and the inter-metapath aggregation to combine messages from multiple metapaths. Extensive experiments on three real-world heterogeneous graph datasets for node classification, node clustering, and link prediction show that MAGNN achieves more accurate prediction results than state-of-the-art baselines.
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Cites
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Inductive Representation Learning on Large Graphs
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Interaction Networks for Learning about Objects, Relations and Physics
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Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
Wang, X., Liu, N., Han, H., & Shi, C. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467415 · Cited by 467
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
Fung, Y., Thomas, C., Gangi Reddy, R., Polisetty, S., Ji, H., Chang, S.-F., McKeown, K., Bansal, M., & Sil, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.133 · Cited by 52
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Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
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Metadata
- Authors
- Fu, X., Zhang, J., Meng, Z., & King, I.
- Venue
- Proceedings of The Web Conference 2020
- Publisher
- ACM
- Published
- 2020-04-20
- Pages
- 2331-2341
- OpenAlex
- W3004507689
- Cited by
- 976
- References
- 8
- FWCI
- 43.24
- Type
- conference paper
- Language
- en
- Open access
- gold
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