Multi-scale link prediction

Donghyuk Shin, Si Si, Inderjit S. Dhillon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


The automated analysis of social networks has become an important problem due to the proliferation of social networks, such as LiveJournal, Flickr and Facebook. The scale of these social networks is massive and continues to grow rapidly. An important problem in social network analysis is proximity estimation that infers the closeness of different users. Link prediction, in turn, is an important application of proximity estimation. However, many methods for computing proximity measures have high computational complexity and are thus prohibitive for large-scale link prediction problems. One way to address this problem is to estimate proximity measures via low-rank approximation. However, a single low-rank approximation may not be sufficient to represent the behavior of the entire network. In this paper, we propose Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can handle massive networks. The basic idea of MSLP is to construct low-rank approximations of the network at multiple scales in an efficient manner. To achieve this, we propose a fast tree-structured approximation algorithm. Based on this approach, MSLP combines predictions at multiple scales to make robust and accurate predictions. Experimental results on real-life datasets with more than a million nodes show the superior performance and scalability of our method.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Number of pages10
StatePublished - 2012
Externally publishedYes
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series


Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI


  • hierarchical clustering
  • link prediction
  • low rank approximation
  • social network analysis

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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