Anchor Link Prediction across Attributed Networks via Network Embedding

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

17 Scopus Citations
View graph of relations

Author(s)

  • Shaokai Wang
  • Xutao Li
  • Yunming Ye
  • Shanshan Feng
  • Xiaohui Huang
  • Xiaolin Du

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number254
Journal / PublicationEntropy
Volume21
Issue number3
Online published6 Mar 2019
Publication statusPublished - Mar 2019

Link(s)

Abstract

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.

Research Area(s)

  • anchor link prediction, network embedding, attributed network

Citation Format(s)

Anchor Link Prediction across Attributed Networks via Network Embedding. / Wang, Shaokai; Li, Xutao; Ye, Yunming et al.
In: Entropy, Vol. 21, No. 3, 254, 03.2019.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Download Statistics

No data available