# 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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Exponential Family Graph Embeddings

- Computer Science, Mathematics
- AAAI
- 2020

This paper introduces the generic \textit{exponential family graph embedding} model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions and demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks. Expand

Topic-aware latent models for representation learning on networks

- Computer Science
- Pattern Recognit. Lett.
- 2021

TNE is introduced, a generic framework to enhance the embeddings of nodes acquired by means of random walk-based approaches with topic-based information, and is able to outperform widely-known baseline NRL models. Expand

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