Kernel Node Embeddings

@article{elikkanat2019KernelNE,
  title={Kernel Node Embeddings},
  author={Abdulkadir Çelikkanat and Fragkiskos D. Malliaros},
  journal={2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
  year={2019},
  pages={1-5}
}
Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classifi-cation. Two popular approaches for this problem include matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning latent node representations. In particular, we propose a weighted matrix factorization model which encodes random walk-based… Expand
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