Dense structural expectation maximisation with parallelisation for efficient large-network structural inference

Christopher Fogelberg, Vasile Palade

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Research on networks is increasingly popular in a wide range of machine learning fields, and structural inference of networks is a key problem. Unfortunately, network structural inference is time consuming and there is an increasing need to infer the structure of ever-larger networks. This article presents the Dense Structural Expectation Maximisation (DSEM) algorithm, a novel extension of the well-known SEM algorithm. DSEM increases the efficiency of structural inference by using the time-expensive calculations required in each SEM iteration more efficiently, and can be O(N) times faster than SEM, where N is the size of the network. The article has also combined DSEM with parallelisation and evaluated the impact of these improvements over SEM, individually and combined. The possibility of combining these novel approaches with other research on structural inference is also considered. The contributions also appear to be usable for all kinds of structural inference, and may greatly improve the range, variety and size of problems which can be tractably addressed. Code is freely available online at: http://syntilect.com/ cgf/pubs:software.

Original languageEnglish
Article number1350011
Number of pages20
JournalInternational Journal on Artificial Intelligence Tools
Volume22
Issue number3
DOIs
Publication statusPublished - Jun 2013
Externally publishedYes

Keywords

  • Bayesian networks
  • large networks
  • parallelisation
  • SEM
  • structural inference

ASJC Scopus subject areas

  • Artificial Intelligence

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