Reconstructing direct and indirect interactions in networked public goods game

Xiao Han, Zhesi Shen, Wen-Xu Wang, Ying-Cheng Lai, Celso Grebogi

Research output: Contribution to journalArticle

12 Citations (Scopus)
5 Downloads (Pure)

Abstract

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances
in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to
solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with
arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.
Original languageEnglish
Article number30241
Pages (from-to)1-12
Number of pages12
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 22 Jul 2016

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