Information propagation game: A tool to acquire humanplaying data for multiplayer influence maximization on social networks

Hung Hsuan Chen, Yan Bin Ciou, Shou De Lin

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

9 Scopus citations

Abstract

With the popularity of online social network services, influence maximization on social networks has drawn much attention in recent years. Most of these studies approximate a greedy based sub-optimal solution by proving the submodular nature of the utility function. Instead of using the analytical techniques, we are interested in solving the diffusion competition and influence maximization problem by a data-driven approach. We propose Information Propagation Game (IPG), a framework that can collect a large number of seed picking strategies for analysis. Through the IPG framework, human players are not only having fun but also helping contributing the seed picking strategies. Preliminary experiment suggests that centrality based heuristics are too simple for seed selection in a multiple player environment.

Original languageEnglish
Title of host publicationKDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages1524-1527
Number of pages4
DOIs
StatePublished - 2012
Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: 12 Aug 201216 Aug 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
Country/TerritoryChina
CityBeijing
Period12/08/1216/08/12

Keywords

  • diffusion network
  • game
  • independent cascade
  • influence maximization
  • information propagation
  • linear threshold

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