Economics at your fingertips  

Structural Factorization of Latent Adjacency Matrix, with an application to Auto Industry Networks

Sayan Chakraborty (), Arnab Bhattacharjee and Taps Maiti
Additional contact information
Sayan Chakraborty: Michigan State University
Arnab Bhattacharjee: Heriot-Watt University
Taps Maiti: Michigan State University

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 1, 185-206

Abstract: Abstract There is substantial interest in the current literature – spanning finance, economics, engineering and medical imaging – on the relationship structure between several nodes in a complex system. In this paper, we extend the literature by developing model and inference for complex networks in terms of latent factors, by extracting the hidden factors that plays significant role in the configuration of inter node relationships. Together, we extend inferences to applications where the underlying network structure is also latent; that is, the adjacency matrix is unobserved. Here, we consider a Bayesian variant of the matrix factorization technique to develop a structural model of the latent adjacency matrix. There are many potential applications. For illustration, we consider a latent network of firms in the in the US automotive sector, where the central object is to understand the impact of an economic shock on firms (or nodes of the network). An important question centers around the factors that affect the stability and resilience of inter node relationships in a network. In the automotive sector application, we would like to know whether these relationship structures are driven by collaborative or competitive environments? What are the effects of a collaborative or competitive role played by a specific firm on the configuration of the relationship network? Using accounting data on firm sales and costs, we use our proposed object oriented factorization methodology to provide explanation of the estimated network links between 3 major US auto manufacturers and their intermediate suppliers.

Keywords: Adjacency matrix; Matrix factorization; Latent space; Error correction model; Spatial error model.; Primary 62-XX; Secondary 62F15 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1007/s13571-019-00202-0

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2021-11-27
Handle: RePEc:spr:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-019-00202-0