Skip to Main content Skip to Navigation
Conference papers

TNE: A Latent Model for Representation Learning on Networks

Abstract : Network representation learning (NRL) methods aim to map each vertex into a low dimensional space by preserving both local and global structure of a given network. In recent years, various approaches based on random walks have been proposed to learn node embeddings-thanks to their success in several challenging problems. In this paper, we introduce a general framework to enhance node embeddings acquired by means of the random walk-based approaches. Similar to the notion of topical word embeddings in NLP, the proposed framework assigns each vertex to a topic with the favor of various statistical models and community detection methods, and then generates the enhanced community representations. We evaluate our method on two downstream tasks: node classification and link prediction. The experimental results demonstrate that the incorporation of vertex and topic embeddings outperform widely-known baseline NRL methods.
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : Abdulkadir Celikkanat Connect in order to contact the contributor
Submitted on : Monday, December 17, 2018 - 3:01:35 PM
Last modification on : Friday, February 4, 2022 - 3:25:25 AM
Long-term archiving on: : Monday, March 18, 2019 - 3:33:09 PM


Files produced by the author(s)


  • HAL Id : hal-01957684, version 1


Abdulkadir Çelikkanat, Fragkiskos Malliaros. TNE: A Latent Model for Representation Learning on Networks. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Relational Representation Learning Workshop, Dec 2018, Montréal, Canada. ⟨hal-01957684⟩



Record views


Files downloads