The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
Abstract
In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs). The core of the proposed framework is a neural network model, also called HIN2Vec, designed to capture the rich semantics embedded in HINs by exploiting different types of relationships among nodes. Given a set of relationships specified in forms of meta-paths in an HIN, HIN2Vec carries out multiple prediction training tasks jointly based on a target set of relationships to learn latent vectors of nodes and meta-paths in the HIN. In addition to model design, several issues unique to HIN2Vec, including regularization of meta-path vectors, node type selection in negative sampling, and cycles in random walks, are examined. To validate our ideas, we learn latent vectors of nodes using four large-scale real HIN datasets, including Blogcatalog, Yelp, DBLP and U.S. Patents, and use them as features for multi-label node classification and link prediction applications on those networks. Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8% of $MAP$ in link prediction.
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Cites
ImageNet classification with deep convolutional neural networks
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node2vec: Scalable Feature Learning for Networks
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Cited by
Heterogeneous Graph Attention Network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191
Heterogeneous Information Network Embedding for Recommendation
Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2833443 · Cited by 1,204
Network Representation Learning: A Survey
Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Yu, P. S. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177455 · Cited by 438
Metadata
- Authors
- Fu, T.-Y., Lee, W.-C., & Lei, Z.
- Venue
- Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2017-11-06
- Pages
- 1797-1806
- OpenAlex
- W2767774008
- Cited by
- 594
- References
- 19
- FWCI
- 25.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fu, T.-Y., Lee, W.-C., & Lei, Z. (2017). HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. https://doi.org/10.1145/3132847.3132953
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