  This paper introduces a novel framework for modeling temporal events with
complex longitudinal dependency that are generated by dependent sources. This
framework takes advantage of multidimensional point processes for modeling time
of events. The intensity function of the proposed process is a mixture of
intensities, and its complexity grows with the complexity of temporal patterns
of data. Moreover, it utilizes a hierarchical dependent nonparametric approach
to model marks of events. These capabilities allow the proposed model to adapt
its temporal and topical complexity according to the complexity of data, which
makes it a suitable candidate for real world scenarios. An online inference
algorithm is also proposed that makes the framework applicable to a vast range
of applications. The framework is applied to a real world application, modeling
the diffusion of contents over networks. Extensive experiments reveal the
effectiveness of the proposed framework in comparison with state-of-the-art
methods.
