Prediction in economic networks: Using the implicit gestalt in product graphs

Vasant Dhar, Tomer Geva, Gal Oestreicher-Singer, Arun Sundararajan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


We define an economic network as a linked set of products, where links are created by realizations of shared outcomes between entities. We analyze the predictive information contained in an increasingly prevalent type of economic network, a "product network" that links the landing pages of goods frequently co-purchased on e-commerce websites. Our data include one million books in 400 categories spanning two years, with over 70 million observations. Using autoregressive and neural-network models, we demonstrate that combining historical demand of a product with that of its neighbors improves demand predictions even as the network changes over time. Furthermore, network properties such as clustering and centrality contribute significantly to predictive accuracy. To our knowledge, this is the first large-scale study showing that a non-static product network contains useful distributed information for demand prediction, and that this information is more effectively exploited by integrating composite structural network properties into one's predictive models.

Original languageEnglish
Title of host publicationInternational Conference on Information Systems, ICIS 2012
Number of pages18
StatePublished - 2012
EventInternational Conference on Information Systems, ICIS 2012 - Orlando, FL, United States
Duration: 16 Dec 201219 Dec 2012

Publication series

NameInternational Conference on Information Systems, ICIS 2012


ConferenceInternational Conference on Information Systems, ICIS 2012
Country/TerritoryUnited States
CityOrlando, FL


  • Autoregressive models
  • Co-purchase network
  • Network-based prediction
  • Neural networks
  • PageRank
  • Prediction
  • Predictive modeling
  • Product networks


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