  The prediction of information diffusion or cascade has attracted much
attention over the last decade. Most cascade prediction works target on
predicting cascade-level macroscopic properties such as the final size of a
cascade. Existing microscopic cascade prediction models which focus on
user-level modeling either make strong assumptions on how a user gets infected
by a cascade or limit themselves to a specific scenario where "who infected
whom" information is explicitly labeled. The strong assumptions oversimplify
the complex diffusion mechanism and prevent these models from better fitting
real-world cascade data. Also, the methods which focus on specific scenarios
cannot be generalized to a general setting where the diffusion graph is
unobserved.
  To overcome the drawbacks of previous works, we propose a Neural Diffusion
Model (NDM) for general microscopic cascade prediction. NDM makes relaxed
assumptions and employs deep learning techniques including attention mechanism
and convolutional network for cascade modeling. Both advantages enable our
model to go beyond the limitations of previous methods, better fit the
diffusion data and generalize to unseen cascades. Experimental results on
diffusion prediction task over four realistic cascade datasets show that our
model can achieve a relative improvement up to 26% against the best performing
baseline in terms of F1 score.
