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

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 publicationAAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
PublisherAI Access Foundation
Pages1294-1299
Number of pages6
ISBN (Print)9781577354666
StatePublished - 2010
Event24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, GA, United States
Duration: Jul 11 2010Jul 15 2010

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Other

Other24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
Country/TerritoryUnited States
CityAtlanta, GA
Period7/11/107/15/10

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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