Figure 2
Model evaluation.
(a) AUC of the Decay model and baselines. AUC measures the area under the ROC curves and thus is equivalent to the probability that a trained model correctly distinguish a randomly selected positive example from another randomly selected negative example. (b) Perplexity of the Decay model and baselines when predicting retweeting behaviors, against the training set ratio. A lower perplexity indicates a better prediction accuracy, meaning less extent a testing example surprises a trained model. (c) Receiver Operating Characteristic (ROC) curves with a training set of 90% examples. (d) Influence spreads of an initial seed set selected on propagation probabilities predicted by the Decay model and baselines.
