Dense Structural Expectation Maximisation with Parallelisation for Efficient Large-Network Structural Inference

Abstract: Research on networks is increasingly important 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 DSEM, a novel extension of the 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 their impact, 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/pubs-software

  @Article{fogelbergPalade2013a,
    author = 	 {Christopher Fogelberg and Vasile Palade},
  title = 	 {Dense Structural Expectation Maximisation with Parallelisation for Efficient Large-Network Structural Inference},
  journal = 	 {IJAIT},
  year = 	 {2013},
  note =         {accepted January 21, 2013}
}

Available here.