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The information diffusion prediction on social networks aims to predictfuture recipients of a message, with practical applications in marketing andsocial media. While different prediction models all claim to perform well,general frameworks for performance evaluation remain limited. Here, we aim toidentify a performance characteristic curve for a model, which captures itsperformance on tasks of different complexity. We propose a metric based oninformation entropy to quantify the randomness in diffusion data, then identifya scaling pattern between the randomness and the prediction accuracy of themodel. Data points in the patterns by different sequence lengths, system sizes,and randomness all collapse into a single curve, capturing a model's inherentcapability of making correct predictions against increased uncertainty. Giventhat this curve has such important properties that it can be used to evaluatethe model, we define it as the performance characteristic curve of the model.The validity of the curve is tested by three prediction models in the samefamily, reaching conclusions in line with existing studies. Also, the curve issuccessfully applied to evaluate two distinct models from the literature. Ourwork reveals a pattern underlying the data randomness and prediction accuracy.The performance characteristic curve provides a new way to systematicallyevaluate models' performance, and sheds light on future studies on otherframeworks for model evaluation.

A performance characteristic curve for model evaluation: the application in information diffusion prediction