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SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

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Résumé

Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.

langue originaleAnglais
titreAdvances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
rédacteurs en chefHady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
EditeurSpringer
Pages487-498
Nombre de pages12
ISBN (imprimé)9783030474256
Les DOIs
étatPublié - 1 janv. 2020
Evénement24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapour
Durée: 11 mai 202014 mai 2020

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12084 LNAI
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
Pays/TerritoireSingapour
La villeSingapore
période11/05/2014/05/20

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