2015/10/04 by Ali Zarezade, Zarezade, Ali, Ali Khodadadi +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Diffusion and Search Dynamics #FOS: Computer and information sciences #Point processes and geometric inequalities #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1510.00936
openalex publication_date 2015/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.