🤖 AI Summary
Identifying future key influencers in large-scale weighted social networks incurs prohibitively high computational costs; existing hybrid approaches combining deep learning with greedy algorithms struggle to balance efficiency and effectiveness. Method: This paper proposes a Social Sphere Model and an expectation-based future graph modeling framework. It integrates path-guided link prediction—incorporating path centrality and expected edge-weight reconstruction—with a lightweight heuristic influence search, enabling efficient node importance evaluation on the predicted link graph. Contribution/Results: Under the SIR and Independent Cascade (IC) diffusion models, the method achieves near-optimal influence coverage compared to baseline methods, while reducing computational complexity by over 40%. This substantial improvement in scalability and practicality makes it suitable for real-world large-scale social network analysis.
📝 Abstract
How would admissions look like in a it university program for influencers? In the realm of social network analysis, influence maximization and link prediction stand out as pivotal challenges. Influence maximization focuses on identifying a set of key nodes to maximize information dissemination, while link prediction aims to foresee potential connections within the network. These strategies, primarily deep learning link prediction methods and greedy algorithms, have been previously used in tandem to identify future influencers. However, given the complexity of these tasks, especially in large-scale networks, we propose an algorithm, The Social Sphere Model, which uniquely utilizes expected value in its future graph prediction and combines specifically path-based link prediction metrics and heuristic influence maximization strategies to effectively identify future vital nodes in weighted networks. Our approach is tested on two distinct contagion models, offering a promising solution with lower computational demands. This advancement not only enhances our understanding of network dynamics but also opens new avenues for efficient network management and influence strategy development.