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Influence maximization under the linear threshold model on a CMOS Ising solver.

Ziqing Zeng1, S Ramprasath2, Hüsrev Cılasun1

  • 1University of Minnesota, Minneapolis, USA.

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Summary
This summary is machine-generated.

We present a novel Ising formulation for the Linear Threshold Influence Maximization (IM) problem on directed acyclic graphs. This approach leverages CMOS Ising solvers for energy-efficient and superior solvability compared to existing methods.

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Area of Science:

  • Network Science
  • Computational Science
  • Artificial Intelligence

Background:

  • Influence Maximization (IM) is crucial for applications like viral marketing and cybersecurity.
  • Classical IM solvers face challenges with NP-hard models, balancing solution quality and runtime.
  • The Linear Threshold model offers a more tractable alternative, amenable to Ising formulations.

Purpose of the Study:

  • To develop a novel workflow for solving the Linear Threshold IM problem using CMOS Ising solvers.
  • To adapt the Ising formulation to hardware constraints through decomposition and preprocessing.
  • To evaluate the efficiency and energy consumption of the proposed approach.

Main Methods:

  • Integer linear programming-based Ising formulation for the Linear Threshold model.
  • Hardware-aware decomposition and preprocessing techniques.
  • Evaluation on directed acyclic graphs using CMOS Ising solvers.

Main Results:

  • The proposed approach demonstrates superior solvability compared to state-of-the-art IM solvers.
  • Competitive runtime performance was maintained.
  • Orders-of-magnitude reduction in energy consumption was achieved.

Conclusions:

  • Ising solvers show significant potential for energy-efficient Influence Maximization.
  • The novel workflow effectively addresses the Linear Threshold IM problem on directed acyclic graphs.
  • This method offers a promising direction for future research in network science and AI.