HNP3: A Hierarchical Nonparametric Point Process for Modeling Content Diffusion over Social Media

TL;DR

This paper presents HNP3, a hierarchical nonparametric point process model that adaptively captures complex temporal and topical dependencies in content diffusion over social media, with an efficient online inference algorithm.

Contribution

The paper introduces a novel hierarchical nonparametric point process framework with online inference for modeling complex content diffusion in social networks.

Findings

Outperforms state-of-the-art methods in real-world content diffusion tasks.

Effectively captures complex temporal and topical dependencies.

Demonstrates adaptability to data complexity through hierarchical modeling.

Abstract

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.