Influence Spread in the Heterogeneous Multiplex Linear Threshold Model

Yaofeng Desmond Zhong, Vaibhav Srivastava, Naomi Ehrich Leonard

Research output: Contribution to journalArticlepeer-review

Abstract

The linear threshold model (LTM) has been used to study spread on single-layer networks defined by one inter-agent sensing modality and agents homogeneous in protocol. We define and analyze the heterogeneous multiplex LTM to study spread on multi-layer networks with each layer representing a different sensing modality and agents heterogeneous in protocol. Protocols are designed to distinguish signals from different layers: an agent becomes active if a sufficient number of its neighbors in each of any a of the m layers is active. We focus on Protocol OR, when a=1, and Protocol AND, when a=m, which model agents that are most and least readily activated, respectively. We develop theory and algorithms to compute the size of the spread at steady state for any set of initially active agents and to analyze the role of distinguished sensing modalities, network structure, and heterogeneity. We show how heterogeneity manages the tension in spreading dynamics between sensitivity to inputs and robustness to disturbances.

Original languageEnglish (US)
JournalIEEE Transactions on Control of Network Systems
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization

Keywords

  • Cascade dynamics
  • Computational modeling
  • contagion
  • Control systems
  • heterogeneity
  • multi-agent systems
  • multi-layer networks
  • Multiplexing
  • Protocols
  • Robot sensing systems
  • Sensors
  • social networks
  • Steady-state

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