Task-specific Temporal Node Embedding - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Task-specific Temporal Node Embedding

Résumé

Graph embedding aims to learn a representation of graphs' nodes in a latent low-dimensional space. The purpose is to encode the graph's structural information. While the majority of real-world networks are dynamic, literature generally focuses on static networks and overlooks evolution patterns. In a previous article entitled "TemporalNode2vec: Temporal Node Embedding in Temporal Networks", we introduced a dynamic graph embedding method that learns continuous time-aware vertex representations. In this paper, we adapt TemporalNode2vec to tackle especially the node classification-related tasks. Overall, we prove that task-specific embedding improves data efficiency significantly comparing to task-agnostic embedding.
Fichier principal
Vignette du fichier
taskSpecificTN2V_FRCCS_HAL.pdf (809.51 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03274101 , version 1 (29-06-2021)

Identifiants

  • HAL Id : hal-03274101 , version 1

Citer

Mounir Haddad, Cécile Bothorel, Philippe Lenca, Dominique Bedart. Task-specific Temporal Node Embedding. French Regional Conference on Complex Systems, May 2021, Dijon, France. ⟨hal-03274101⟩
54 Consultations
76 Téléchargements

Partager

Gmail Facebook X LinkedIn More