Abstract
Graph embedding techniques have been introduced in recent years with the aim of mapping graph data into low-dimensional vector spaces, so that conventional machine learning methods can be exploited. In particular, in the DeepWalk model, truncated random walks are employed in random walk-based approaches to capture structural links-connections between nodes. The SkipGram model is then applied to the truncated random walks to compute the embedded nodes. In this work, the proposed DeepWalk model provides a faster convergence speed than the standard one by introducing a new trainable parameter in the model. Furthermore, experimental results on real-world datasets show that the performance in downstream community detection and link prediction task is improved by using the proposed DeepWalk model.
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Notes
- 1.
\(M_{n \times m}\) denotes the set of matrices \(n \times m\).
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Loumponias, K., Kosmatopoulos, A., Tsikrika, T., Vrochidis, S., Kompatsiaris, I. (2023). Modified SkipGram Negative Sampling Model for Faster Convergence of Graph Embedding. In: Fred, A., Sansone, C., Gusikhin, O., Madani, K. (eds) Deep Learning Theory and Applications. DeLTA 2022. Communications in Computer and Information Science, vol 1858. Springer, Cham. https://doi.org/10.1007/978-3-031-37317-6_1
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