most citedGlobal Vectors for Node Representations

47 citations · 64 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20202 cited

New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding

Robin Brochier, Antoine Gourru, Adrien Guille +1

The scientific literature is growing faster than ever. Finding an expert in a particular scientific domain has never been as hard as today because of the increasing amount of publi…

cs.LG2020

Inductive Document Network Embedding with Topic-Word Attention

Robin Brochier, Adrien Guille, Julien Velcin

Document network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding ap…

cs.CL201912 cited

Link Prediction with Mutual Attention for Text-Attributed Networks

Robin Brochier, Adrien Guille, Julien Velcin

In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-P…

cs.CL20193 cited

Representation Learning for Recommender Systems with Application to the Scientific Literature

Robin Brochier

The scientific literature is a large information network linking various actors (laboratories, companies, institutions, etc.). The vast amount of data generated by this network con…

cs.CL201947 cited

Global Vectors for Node Representations

Robin Brochier, Adrien Guille, Julien Velcin

Most network embedding algorithms consist in measuring co-occurrences of nodes via random walks then learning the embeddings using Skip-Gram with Negative Sampling. While it has pr…