SubRank: Subgraph Embeddings via a Subgraph Proximity Measure - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Oana Balalau
Sagar Goyal
  • Fonction : Auteur
  • PersonId : 1090419

Résumé

Representation learning for graph data has gained a lot ofattention in recent years. However, state-of-the-art research is focusedmostly on node embeddings, with little effort dedicated to the closelyrelated task of computing subgraph embeddings. Subgraph embeddingshave many applications, such as community detection, cascade predic-tion, and question answering. In this work, we propose a subgraph to sub-graph proximity measure as a building block for a subgraph embeddingframework. Experiments on real-world datasets show that our approach,SubRank, outperforms state-of-the-art methods on several importantdata mining tasks.
Fichier principal
Vignette du fichier
output(11).pdf (280.27 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03134181 , version 1 (08-02-2021)

Identifiants

Citer

Oana Balalau, Sagar Goyal. SubRank: Subgraph Embeddings via a Subgraph Proximity Measure. PAKDD 2020 - Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2020, Singapore, Singapore. pp.487-498, ⟨10.1007/978-3-030-47426-3_38⟩. ⟨hal-03134181⟩
89 Consultations
259 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More