A general view for network embedding as matrix factorization
© 2019 Association for Computing Machinery. We propose a general view that demonstrates the relationship between network embedding approaches and matrix factorization. Unlike previous works that present the equivalence for the approaches from a skip-gram model perspective, we provide a more fundamen...
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| Main Authors: | , , , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 出版: |
2020
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| 主題: | |
| 在線閱讀: | https://repository.li.mahidol.ac.th/handle/123456789/50652 |
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| 機構: | Mahidol University |
| 總結: | © 2019 Association for Computing Machinery. We propose a general view that demonstrates the relationship between network embedding approaches and matrix factorization. Unlike previous works that present the equivalence for the approaches from a skip-gram model perspective, we provide a more fundamental connection from an optimization (objective function) perspective. We demonstrate that matrix factorization is equivalent to optimizing two objectives: one is for bringing together the embeddings of similar nodes; the other is for separating the embeddings of distant nodes. The matrix to be factorized has a general form: S−β·1. The elements of S indicate pairwise node similarities. They can be based on any user-defined similarity/distance measure or learned from random walks on networks. The shift number β is related to a parameter that balances the two objectives. More importantly, the resulting embeddings are sensitive to β and we can improve the embeddings by tuning β. Experiments show that matrix factorization based on a new proposed similarity measure and β-tuning strategy significantly outperforms existing matrix factorization approaches on a range of benchmark networks. |
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