DeepRank: improving unsupervised node ranking via link discovery

• Lai, Yi-An ^[1] ; Hsu, Chin-Chi ^[2] ; Chen, Wen-Hao ^[1] ; Yeh, Mi-Yen ^[2] ; Lin, Shou-De ^[1]
  1. [1] National Taiwan University

     National Taiwan University

     Taiwán

     
  2. [2] Institute of Information Science and Technologies

     Institute of Information Science and Technologies

     Pisa, Italia

     
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 33, Nº 2, 2019, págs. 474-498
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • This paper proposes an unsupervised node-ranking model that considers not only the attributes of nodes in a graph but also the incompleteness of the graph structure. We formulate the unsupervised ranking task into an optimization task and propose a deep neural network (DNN) structure to solve it. The rich representation capability of the DNN structure together with a novel design of the objectives allow the proposed model to significantly outperform the state-of-the-art ranking solutions.