GTPA: A Generative Model For Online Mentor-Apprentice Networks

Muhammad Aurangzeb Ahmad, David Huffaker, Jing Wang, Jeff Treem, Marshall Scott Poole, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

There is a large body of work on the evolution of graphs in various domains, which shows that many real graphs evolve in a similar manner. In this paper we study a novel type of network formed by mentor-apprentice relationships in a massively multiplayer online role playing game. We observe that some of the static and dynamic laws which have been observed in many other real world networks are not observed in this network. Consequently well known graph generators like Preferential Attachment, Forest Fire, Butterfly, RTM, etc., cannot be applied to such mentoring networks. We propose a novel generative model to generate networks with the characteristics of mentoring networks.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
PublisherAAAI Press
Pages1294-1299
Number of pages6
ISBN (Electronic)9781577354642
StatePublished - Jul 15 2010
Event24th AAAI Conference on Artificial Intelligence, AAAI 2010 - Atlanta, United States
Duration: Jul 11 2010Jul 15 2010

Publication series

NameProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Conference

Conference24th AAAI Conference on Artificial Intelligence, AAAI 2010
Country/TerritoryUnited States
CityAtlanta
Period7/11/107/15/10

ASJC Scopus subject areas

  • Artificial Intelligence

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