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Choosing Optimal Seed Nodes in Competitive Contagion.

Prem Kumar1, Puneet Verma1, Anurag Singh1

  • 1Department of Computer Science and Engineering, National Institute of Technology Delhi, New Delhi, India.

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Choosing initial advertisers in competitive markets involves balancing node influence and quantity. This study identifies optimal seed node strategies for maximizing product influence against rivals.

Keywords:
centrality measurescompetitive contagioncompetitive marketingcomplex networksgame theoryseed nodes

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Area of Science:

  • Network Science
  • Marketing Strategy
  • Computational Social Science

Background:

  • Growing interest in simulating competitive markets for advertising and ideology spread.
  • Focus on binary competitive contagion processes with simultaneous diffusion in networks.

Purpose of the Study:

  • Investigate optimal centrality measures for identifying seed nodes in competitive scenarios.
  • Determine the most effective strategy for firms to maximize influence against rivals.
  • Analyze the trade-off between selecting a few highly influential nodes versus many less influential ones.

Main Methods:

  • Modeling a binary competitive contagion process on a network.
  • Evaluating various centrality measures to identify influential seed nodes.
  • Simulating diffusion processes under fixed budgets and varying node prices.

Main Results:

  • Identified specific centrality measures that are superior for selecting seed nodes in competitive environments.
  • Demonstrated that the optimal strategy (few influential vs. many less influential nodes) depends on network structure and budget constraints.
  • Quantified the impact of node pricing on the selection of initial advertisers.

Conclusions:

  • The choice of centrality measure significantly impacts the effectiveness of seed node selection in competitive marketing.
  • Firms must consider network topology, node importance, and budget when deciding on the number and influence of initial advertisers.
  • This research provides a framework for optimizing marketing strategies in competitive network environments.