Alex Koong

Models of social media spread have either prescribed a fixed growth function or assumed a fixed way in which past activity fades. A fractional physics-informed neural network writes each post's engagement rate as a per-post viral potential times one shared growth law, with a learnable fractional derivative setting how strongly history matters. On 348 training and 86 test posts from X, splitting the 24-hour window into four separately trained segments let the model reconstruct 64% of held-out trajectories within a mean squared error of 0.01, against 44% for Bass diffusion, the strongest of nine classical baselines.

Models of social media spread have either prescribed a fixed growth function or assumed a fixed way in which past activity fades. A fractional physics-informed neural network writes each post's engagement rate as a per-post viral potential times one shared growth law, with a learnable fractional derivative setting how strongly history matters. On 348 training and 86 test posts from X, splitting the 24-hour window into four separately trained segments let the model reconstruct 64% of held-out trajectories within a mean squared error of 0.01, against 44% for Bass diffusion, the strongest of nine classical baselines.