The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
REGAL: Representation Learning-based Graph Alignment
Open accessAbstract
Problems involving multiple networks are prevalent in many scientific and other domains. In particular, network alignment, or the task of identifying corresponding nodes in different networks, has applications across the social and natural sciences. Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs. Within REGAL we devise xNetMF, an elegant and principled node embedding formulation that uniquely generalizes to multi-network problems. Our results demonstrate the utility and promise of unsupervised representation learning-based network alignment in terms of both speed and accuracy. REGAL runs up to 30x faster in the representation learning stage than comparable methods, outperforms existing network alignment methods by 20 to 30% accuracy on average, and scales to networks with millions of nodes each.
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DeepWalk: online learning of social representations
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Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
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Metadata
- Authors
- Heimann, M., Shen, H., Safavi, T., & Koutra, D.
- Venue
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2018-10-17
- Pages
- 117-126
- OpenAlex
- W2888657195
- Cited by
- 257
- References
- 23
- FWCI
- 11.13
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Heimann, M., Shen, H., Safavi, T., & Koutra, D. (2018). REGAL: Representation Learning-based Graph Alignment. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. https://doi.org/10.1145/3269206.3271788
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