A4 Article in conference proceedings
Sampled Fictitious Play on Networks (2019)


Nikolaev, A., Semenov, A., & Pasiliao, E. L. (2019). Sampled Fictitious Play on Networks. In A. Tagarelli, & H. Tong (Eds.), CSoNet 2019 : 8th International Conference on Computational Data and Social Networks, Proceedings (pp. 33-44). Springer. Lecture Notes in Computer Science, 11917. https://doi.org/10.1007/978-3-030-34980-6_3


JYU authors or editors


Publication details

All authors or editors: Nikolaev, Alexander; Semenov, Alexander; Pasiliao, Eduardo L.

Parent publication: CSoNet 2019 : 8th International Conference on Computational Data and Social Networks, Proceedings

Parent publication editors: Tagarelli, Andrea; Tong, Hanghang

Conference:

  • International Conference on Computational Data and Social Networks

Place and date of conference: Ho Chi Minh City, Vietnam, 18.-20.11.2019

ISBN: 978-3-030-34979-0

eISBN: 978-3-030-34980-6

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2019

Number in series: 11917

Pages range: 33-44

Number of pages in the book: 372

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-34980-6_3

Publication open access: Not open

Publication channel open access:


Abstract

We formulate and solve the problem of optimizing the structure of an information propagation network between multiple agents. In a given space of interests (e.g., information on certain targets), each agent is defined by a vector of their desirable information, called filter, and a vector of available information, called source. The agents seek to build a directed network that maximizes the value of the desirable source-information that reaches each agent having been filtered en route, less the expense that each agent incurs in filtering any information of no interest to them. We frame this optimization problem as a game of common interest, where the Nash equilibria can be attained as limit points of Sampled Fictitious Play (SFP), offering a method that turns out computationally effective in traversing the huge space of feasible networks on a given node set. Our key idea lies in the creative use of history in SFP, leading to the new History Value-Weighted SFP method. To our knowledge, this is the first successful application of FP for network structure optimization. The appeal of our work is supported by the outcomes of the computational experiments that compare the performance of several algorithms in two settings: centralized (full information) and decentralized (local information).


Keywords: social networks; flow of information; optimisation; network theory; game theory

Free keywords: social networks; information diffusion; fictitious play


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2021-09-06 at 00:54