社会球体模型：演化网络中的启发式影响力预测
The Social Sphere Model: Heuristic Influence Prediction in Evolving Networks
AI总结：
本文提出社会球体模型，结合期望值、基于路径的链路预测指标和启发式影响力最大化策略，以较低计算成本有效识别加权网络中的未来关键节点。
摘要：
如果有一个面向网红的大学项目，其录取情况会是什么样？在社交网络分析领域，影响力最大化和链路预测是两个关键挑战。影响力最大化侧重于识别一组关键节点以最大化信息传播，而链路预测旨在预见网络中的潜在连接。这些策略，主要是深度学习链路预测方法和贪心算法，此前已被结合使用以识别未来的网红。然而，鉴于这些任务的复杂性，尤其是在大规模网络中，我们提出了一种算法——社会球体模型（The Social Sphere Model），该模型在其未来图预测中独特地利用了期望值，并结合了基于路径的链路预测指标和启发式影响力最大化策略，以有效识别加权网络中的未来关键节点。我们的方法在两个不同的传播模型上进行了测试，提供了一种计算需求更低的、有前景的解决方案。这一进展不仅增强了我们对网络动力学的理解，也为高效的网络管理和影响力策略开发开辟了新途径。
英文摘要：
How would admissions look like in a 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
