流行度比率最大化：通过影响传播超越竞争对手
Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation
AI总结：
提出融合 PA 与 IC 的 PA-IC 模型及 PRM 问题，借助单调子模代理目标设计两种算法，PRM-IMM 可稳定实现最佳流行度提升。
摘要：
本文针对如何通过社交网络中的策略性推广在流行度上超越竞争对手展开算法研究。我们首先提出一个新颖模型，将用于流行度增长的 Preferential Attachment（PA，优先连接）模型与用于社交网络影响传播的 Independent Cascade（IC，独立级联）模型相结合，称为 PA-IC 模型。在 PA-IC 中，一个流行物品和一个新物品通过 PA 模型从自然流行度增长中获取流行度份额，同时新物品试图借助社交网络中的影响级联获得额外流行度。流行度比率定义为新物品与流行物品之间流行度度量的比值。我们将 Popularity Ratio Maximization（PRM，流行度比率最大化）形式化为通过多轮选择种子以最终最大化流行度比率的问题。我们分析了流行度比率，并证明它具有单调性但不具备子模性。为提供有效解决方案，我们设计了一个代理目标函数；实验表明它与原始目标函数非常接近，而在理论上它同时具有单调性和子模性。我们设计了两种高效算法：一种适用于影响重叠但种子（跨轮次）不重叠的设置，另一种适用于影响不重叠但种子重叠的设置，并进一步讨论了如何处理其他模型和问题变体。实验评估进一步表明，与其他方法相比，所提出的 PRM-IMM 方法始终取得最佳的流行度提升效果。我们的理论与实验分析揭示了社交网络中影响最大化与优先连接之间的相互作用。
英文摘要：
In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The popularity ratio is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate Popularity Ratio Maximization (PRM) as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that the proposed PRM-IMM method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.
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。
