Seeding a Simple Contagion
提出一种选择种子以最大化传染病传播的方法，通过粗分类个体拟合随机图模型，计算每类种子的平均新感染数，选择最高乘数的类别进行播种，相比常见方法所需数据更粗、计算量更小，并用真实网络数据模拟验证。
I propose a method for selecting seeds to maximize contagion. First, fit a random graph model using a coarse categorization of individuals. Next, compute a seed multiplier for each category—this is the average number of new infections a seed generates. Finally, seed the category with the highest multiplier. Relative to the most common methods, my approach requires far less granular data, and it consumes less computing power—the problem scales with the number of categories, not the number of individuals. I validate the methodology through simulations using real network data.