HNP3: A Hierarchical Nonparametric Point Process for Modeling Content Diffusion over Social Media
This work addresses the problem of accurately modeling content diffusion over social media for researchers and practitioners, representing an incremental improvement with a novel hybrid method.
The paper tackles modeling temporal events with complex dependencies from multiple sources, introducing a hierarchical nonparametric point process framework that adapts to data complexity, and demonstrates its effectiveness in content diffusion modeling with superior performance over state-of-the-art methods.
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.