Edge selection for undirected graphs

Meng Hwee Victor Ong, Sanjay Chaudhuri, Berwin A. Turlach

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This article explores an ‘Edge Selection’ procedure to fit an undirected graph to a given data set. Undirected graphs are routinely used to represent, model and analyse associative relationships among the entities on a social, biological or genetic network. Our proposed method combines the computational efficiency of least angle regression and at the same time ensures symmetry of the selected adjacency matrix. Various local and global properties of the edge selection path are explored analytically. In particular, a suitable parameter that controls the amount of shrinkage is identified and we consider several cross-validation techniques to choose an accurate predictive model on the path. The proposed method is illustrated with a detailed simulation study involving models with various levels of sparsity and variability in the nodal degree distributions. Finally, our method is used to select undirected graphs from various real data sets. We employ it for identifying the regulatory network of isoprenoid pathways from a gene-expression data and also to identify genetic network from a high-dimensional breast cancer study data.

Original languageEnglish
Pages (from-to)3291-3322
Number of pages32
JournalJournal of Statistical Computation and Simulation
Issue number17
Publication statusPublished - 22 Nov 2018


Dive into the research topics of 'Edge selection for undirected graphs'. Together they form a unique fingerprint.

Cite this