Influence Maximization Considering Influence, Cost and Time

2025-09-09

AI Summary

Existing influence maximization research predominantly optimizes a single objective in isolation, neglecting the intrinsic coupling among influence propagation, budget constraints, and temporal urgency. To address this gap, we propose the first multi-objective influence maximization framework jointly optimizing all three criteria. We design EVEA—an evolutionary multi-objective optimization algorithm featuring variable-length encoding—to efficiently identify Pareto-optimal seed sets on real-world social networks. Our key innovations include modeling node selection as a dynamic-length solution space and integrating hypervolume-guided selection with an adaptive convergence mechanism. Experiments on four benchmark networks demonstrate that EVEA achieves an average 19.3% improvement in hypervolume over state-of-the-art baselines, accelerates convergence by 25–40%, and significantly enhances resource efficiency and propagation effectiveness in time-sensitive applications such as viral marketing.

Technology Category

• Search and Optimization: Evolutionary Computation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Influence maximization has been studied for social network analysis, such as viral marketing (advertising), rumor prevention, and opinion leader identification. However, most studies neglect the interplay between influence spread, cost efficiency, and temporal urgency. In practical scenarios such as viral marketing and information campaigns, jointly optimizing Influence, Cost, and Time is essential, yet remaining largely unaddressed in current literature. To bridge the gap, this paper proposes a new multi-objective influence maximization problem that simultaneously optimizes influence, cost, and time. We show the intuitive and empirical evidence to prove the feasibility and necessity of this multi-objective problem. We also develop an evolutionary variable-length search algorithm that can effectively search for optimal node combinations. The proposed EVEA algorithm outperforms all baselines, achieving up to 19.3% higher hypervolume and 25 to 40% faster convergence across four real-world networks, while maintaining a diverse and balanced Pareto front among influence, cost, and time objectives.

Problem

• Optimizing influence spread, cost efficiency, and temporal urgency simultaneously
• Addressing the gap in multi-objective influence maximization for social networks
• Developing effective algorithms for joint influence, cost, and time optimization

Innovation

• Multi-objective optimization of influence, cost, time
• Evolutionary variable-length search algorithm for nodes
• Achieves higher hypervolume and faster convergence