Exploring expert cognition for atributed network embedding

Xiao Huang, Qingquan Song, Jundong Li, Xia Hu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)

Abstract

Attributed network embedding has been widely used in modeling real-world systems. The obtained low-dimensional vector representations of nodes preserve their proximity in terms of both network topology and node attributes, upon which different analysis algorithms can be applied. Recent advances in explanation-based learning and human-in-the-loop models show that by involving experts, the performance of many learning tasks can be enhanced. It is because experts have a better cognition in the latent information such as domain knowledge, conventions, and hidden relations. It motivates us to employ experts to transform their meaningful cognition into concrete data to advance network embedding. However, learning and incorporating the expert cognition into the embedding remains a challenging task. Because expert cognition does not have a concrete form, and is difficult to be measured and laborious to obtain. Also, in a real-world network, there are various types of expert cognition such as the comprehension of word meaning and the discernment of similar nodes. It is nontrivial to identify the types that could lead to a significant improvement in the embedding. In this paper, we study a novel problem of exploring expert cognition for attributed network embedding and propose a principled framework NEEC. We formulate the process of learning expert cognition as a task of asking experts a number of concise and general queries. Guided by the exemplar theory and prototype theory in cognitive science, the queries are systematically selected and can be generalized to various real-world networks. The returned answers from the experts contain their valuable cognition. We model them as new edges and directly add into the attributed network, upon which different embedding methods can be applied towards a more informative embedding representation. Experiments on real-world datasets verify the effectiveness and efficiency of NEEC.

Original languageEnglish
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages270-278
Number of pages9
ISBN (Electronic)9781450355810
DOIs
Publication statusPublished - 2 Feb 2018
Externally publishedYes
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: 5 Feb 20189 Feb 2018

Publication series

NameWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
Volume2018-Febuary

Conference

Conference11th ACM International Conference on Web Search and Data Mining, WSDM 2018
Country/TerritoryUnited States
CityMarina Del Rey
Period5/02/189/02/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

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