  The prediction for information diffusion on social networks has great
practical significance in marketing and public opinion control. It aims to
predict the individuals who will potentially repost the message on the social
network. One type of method is based on demographics, complex networks and
other prior knowledge to establish an interpretable model to simulate and
predict the propagation process, while the other type of method is completely
data-driven and maps the nodes to a latent space for propagation prediction.
Existing latent space design and embedding methods lack consideration for the
intervene among users. In this paper, we propose an independent asymmetric
embedding method to embed each individual into one latent influence space and
multiple latent susceptibility spaces. Based on the similarity between
information diffusion and heat diffusion phenomenon, the heat diffusion kernel
is exploited in our model and establishes the embedding rules. Furthermore, our
method captures the co-occurrence regulation of user combinations in cascades
to improve the calculating effectiveness. The results of extensive experiments
conducted on real-world datasets verify both the predictive accuracy and
cost-effectiveness of our approach.
