In real world social networks, there are multiple cascades which are rarely
independent. They usually compete or cooperate with each other. Motivated by
the reinforcement theory in sociology we leverage the fact that adoption of a
user to any behavior is modeled by the aggregation of behaviors of its
neighbors. We use a multidimensional marked Hawkes process to model users
product adoption and consequently spread of cascades in social networks. The
resulting inference problem is proved to be convex and is solved in parallel by
using the barrier method. The advantage of the proposed model is twofold; it
models correlated cascades and also learns the latent diffusion network.
Experimental results on synthetic and two real datasets gathered from Twitter,
URL shortening and music streaming services, illustrate the superior
performance of the proposed model over the alternatives.

本研究利用多维标记 Hawkes 过程模拟了社交网络中用户的行为传播，解决了相关级联效应的建模和潜在传播网络的提取问题。通过 Twitter、网址缩短和音乐流媒体服务的实验结果表明，该模型在性能上胜过了其他模型。