On Learning Community-specific Similarity Metrics for Cold-start Link Prediction

Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu

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


This paper studies a cold-start problem of inferring new edges between vertices with no demonstrated edges but vertex content by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in real-world social networks. Because communities imply the existence of local homogeneities, learning a global similarity metric is not appropriate. In this paper, we thus learn community-specific similarity metrics by proposing a community-weighted formulation of metric learning model. To better illustrate the community-weighted formulation, we instantiate it in two models, which are community-weighted ranking (CWR) model and community-weighted probability (CWP) model. Experiments on three real-world networks show that community-specific similarity metrics are meaningful and that both models perform better than those leaning global metrics in terms of prediction accuracy.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
Publication statusPublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CityRio de Janeiro

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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