The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Asymmetric Transitivity Preserving Graph Embedding
Abstract
Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if there is a directed path from u to v, then there is likely a directed edge from u to v. Asymmetric transitivity can help in capturing structures of graphs and recovering from partially observed graphs. To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity. In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. More specifically, we first derive a general formulation that cover multiple popular high-order proximity measurements, then propose a scalable embedding algorithm to approximate the high-order proximity measurements based on their general formulation. Moreover, we provide a theoretical upper bound on the RMSE (Root Mean Squared Error) of the approximation. Our empirical experiments on a synthetic dataset and three real-world datasets demonstrate that HOPE can approximate the high-order proximities significantly better than the state-of-art algorithms and outperform the state-of-art algorithms in tasks of reconstruction, link prediction and vertex recommendation.
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Heterogeneous Graph Attention Network
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Metadata
- Authors
- Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W.
- Venue
- Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2016-08-13
- Pages
- 1105-1114
- OpenAlex
- W2387462954
- Cited by
- 1,248
- References
- 33
- FWCI
- 54.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Funders
- National Program on Key Basic Research Project (2015CB352300)
- National Natural Science Foundation of China (No. 61370022, No. 61531006, No. 61472444 and No. 61210008)
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- Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016). Asymmetric Transitivity Preserving Graph Embedding. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939751
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