TNE: A Latent Model for Representation Learning on Networks

Abdulkadir Çelikkanat (1, 2, 3) , Fragkiskos Malliaros (1, 2, 3)

1 CVN - Centre de vision numérique
2 GALEN-POST - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 CentraleSupélec

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.

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Réseaux sociaux et d'information [cs.SI]

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⟩