The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ( $$\text{OAS}$$ ) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates $$\text{OAS}$$ under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

中文翻译：

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测量网络播种的准确链接预测的价值。

影响力最大化的文献寻求在社会网络中的结构位置可以驱动大量行为的一小部分个体。寻找最佳种子集的优化工作通常假设完全了解网络拓扑。不幸的是，社交网络链接很少以确切的方式知道。基于不太准确的链接预测的播种策略何时提供有价值的见解？我们引入了针对样本优化（$$\text{OAS}$$）的性能，以基于对网络的噪声观察来衡量优化播种的价值。我们的计算研究在合成和现实世界网络中的几个阈值扩展模型下调查 $$\text{OAS}$$。我们的重点是衡量不精确链接信息的价值。具有战略意义的链路预测投资水平似乎密切依赖于传播模型：在某些参数范围内，对改进链路预测的投资可以在级联规模上支付大量溢价。对于其他范围，此类投资将被浪费。几个趋势在拓扑中非常一致。