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- Title
Layer imbalance-aware multiplex network embedding.
- Authors
Chen, Ke-Jia; Qiu, Yinchu; Liu, Zheng; Mu, Wenhui
- Abstract
Multiplex network embedding is an effective technique to jointly learn the low-dimensional representations of nodes across network layers. However, the number of edges among layers may vary significantly. This data imbalance will lead to performance degradation especially on the sparse layer due to learning bias and the adverse effects of irrelevant or conflicting data in other layers. In this paper, a layer imbalance-aware multiplex network embedding (LIAMNE) method is proposed where the edges in auxiliary layers are filtered out under the guidance of the embedding distribution from the target layer in order to minimize noisy relations that are less relevant to the target layer. The method can also balance the number of edges among layers, which is more conductive to learning the sparse target layer. Real-world datasets with different degrees of layer imbalance are used for experimentation. The results demonstrate that LIAMNE significantly outperforms several state-of-the-art multiplex network embedding methods in link prediction on the target layer. Meantime, the comprehensive representation of the entire multiplex network is not compromised by the sampling method as evaluated by its performance on the node classification task.
- Subjects
SAMPLING methods; SPARSE graphs
- Publication
Knowledge & Information Systems, 2024, Vol 66, Issue 6, p3547
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-024-02072-z