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Maximizing User Engagement in Social Networks: A Game-Theoretic Approach to Network Participation and Resource Sharing

Ahmed Luqman, Hassan Jaleel

TL;DR

This work proposes a game-theoretic framework to model and optimize user engagement in cooperative activities over social networks by incorporating stochastic decision models like LLL, and demonstrates that the model aligns with theoretical predictions from existing analytical frameworks and empirical observations across various initial network configurations.

Abstract

We propose a game-theoretic framework to model and optimize user engagement in cooperative activities over social networks. While traditional diffusion models suggest that individuals are only influenced by their neighbors, empirical evidence shows that diffusion alone does not fully explain network evolution, and non-diffusion factors play a significant role in network growth. We model network participation and resource-sharing as strategic games involving boundedly rational players to address this gap between the analytical models and empirical evidence. Specifically, we employ Log-Linear Learning (LLL), a version of noisy best response, to capture players' decision-making strategies. By incorporating stochastic decision models like LLL, our framework integrates both diffusion and non-diffusion dynamics into network evolution dynamics. Through equilibrium analysis and simulations, we demonstrate that our model aligns with theoretical predictions from existing analytical frameworks and empirical observations across various initial network configurations. Our second contribution is a novel method for selecting anchor nodes to enhance user participation. This approach allows system designers to identify anchor nodes and compute their incentives in real time under a more realistic information requirement constraints as compared to the existing approaches. The proposed approach adapts to changing network conditions by reallocating resources from less impactful to more influential nodes. Furthermore, the method is resilient to anchor node failures, ensuring sustained and continuous network participation.

Maximizing User Engagement in Social Networks: A Game-Theoretic Approach to Network Participation and Resource Sharing

TL;DR

This work proposes a game-theoretic framework to model and optimize user engagement in cooperative activities over social networks by incorporating stochastic decision models like LLL, and demonstrates that the model aligns with theoretical predictions from existing analytical frameworks and empirical observations across various initial network configurations.

Abstract

We propose a game-theoretic framework to model and optimize user engagement in cooperative activities over social networks. While traditional diffusion models suggest that individuals are only influenced by their neighbors, empirical evidence shows that diffusion alone does not fully explain network evolution, and non-diffusion factors play a significant role in network growth. We model network participation and resource-sharing as strategic games involving boundedly rational players to address this gap between the analytical models and empirical evidence. Specifically, we employ Log-Linear Learning (LLL), a version of noisy best response, to capture players' decision-making strategies. By incorporating stochastic decision models like LLL, our framework integrates both diffusion and non-diffusion dynamics into network evolution dynamics. Through equilibrium analysis and simulations, we demonstrate that our model aligns with theoretical predictions from existing analytical frameworks and empirical observations across various initial network configurations. Our second contribution is a novel method for selecting anchor nodes to enhance user participation. This approach allows system designers to identify anchor nodes and compute their incentives in real time under a more realistic information requirement constraints as compared to the existing approaches. The proposed approach adapts to changing network conditions by reallocating resources from less impactful to more influential nodes. Furthermore, the method is resilient to anchor node failures, ensuring sustained and continuous network participation.

Paper Structure

This paper contains 27 sections, 57 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Setup
  3. Network Engagement Games
  4. Network Participation Game
  5. Network Resource Sharing Game
  6. Analysis
  7. Network Participation Game
  8. Network Resource Sharing Game
  9. Simulation
  10. Network Participation Game
  11. Network Resource Sharing Game
  12. Stochastic Stability Analysis of Network Participation Game
  13. Radius CoRadius Analysis
  14. Line Graph
  15. Ring Network
  16. ...and 12 more sections

Figures (8)

  • Figure 1: Simulation results for the network participation and resource sharing Games
  • Figure 2: Minimum resistance paths between Nash equilibria for Ring and Wheel networks. The numbers on the edges represent transition resistance.
  • Figure 3: A minimum resistance path from $\sigma^{(0)}$ to $\sigma^{(n)}$ for $CR(\sigma^{(n)}$. Empty nodes represent players who are not participating, red nodes with dark boundaries represent players who are participating as noisy action, and red nodes with regular boundaries represent players who started to participate as their best action.
  • Figure 4: A minimum resistance path from $\sigma^{(n)}$ to $\sigma^{(0)}$ for $CR(\sigma^{(0)})$. Solid nodes (red color) represent participating players, empty nodes with regular boundaries represent players who left the network as noisy action, and empty nodes with dashed/dotted lines represent players who left the network as their optimal action.
  • Figure 5: Simulation results of $Rd-CR$ analysis of Nework participation game for wheel (left) and grid (right) networks. For the wheel network (left), $n = 20$, $T = 0.042$, and $\epsilon = 0.1$. For the $5\times5$ grid network, $T = 0.1$ and $\epsilon = 0.01$.
  • ...and 3 more figures

Theorems & Definitions (6)

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