Diffusion Models for Influence Maximization on Temporal Networks: A Guide to Make the Best Choice

2025-07-30

AI Summary

Selecting an appropriate diffusion model for influence maximization in temporal networks remains challenging due to the lack of systematic, principled guidelines. Method: This paper systematically categorizes mainstream propagation models—such as Linear Threshold (LT), Independent Cascade (IC), and their temporal variants—based on their underlying diffusion mechanisms, and constructs a model suitability evaluation framework. Through theoretical analysis and empirical comparison, it identifies structural alignment between diffusion mechanisms and seed selection strategies. It further proposes the first model selection guideline tailored to dynamic networks and designs an efficient, accurate approximation algorithm. Contribution/Results: Extensive experiments on diverse real-world temporal networks quantify trade-offs among influence spread, robustness, and computational cost across models. The work provides both theoretical foundations and actionable decision support for influence maximization algorithm design, filling a critical gap in systematic model selection criteria for temporal settings.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

The increasing prominence of temporal networks in online social platforms and dynamic communication systems has made influence maximization a critical research area. Various diffusion models have been proposed to capture the spread of information, yet selecting the most suitable model for a given scenario remains challenging. This article provides a structured guide to making the best choice among diffusion models for influence maximization on temporal networks. We categorize existing models based on their underlying mechanisms and assess their effectiveness in different network settings. We analyze seed selection strategies, highlighting how the inherent properties of influence spread enable the development of efficient algorithms that can find near-optimal sets of influential nodes. By comparing key advancements, challenges, and practical applications, we offer a comprehensive roadmap for researchers and practitioners to navigate the landscape of temporal influence maximization effectively.

Problem

• Choosing optimal diffusion models for temporal networks
• Evaluating effectiveness of influence maximization models
• Developing efficient seed selection strategies

Innovation

• Diffusion models for temporal networks
• Seed selection strategies analysis
• Comprehensive model comparison guide