Modeling the Popularity of Events on Web by Sparsity and Mutual-Excitation Guided Graph Neural Network

TL;DR

This paper proposes a graph neural network approach to model web event popularity by capturing keyword interactions through self and mutual excitation, improving prediction accuracy and providing interpretability.

Contribution

The paper introduces a novel GNN-based model that incorporates sparsity and mutual excitation for event popularity prediction, along with a new challenge dataset.

Findings

Outperforms state-of-the-art methods on three datasets

Demonstrates the effectiveness of self and mutual excitation modeling

Provides a publicly available dataset for future research

Abstract

The content of a webpage described or posted an event in the cyberspace inevitably reflects viewpoints, values and trends of the physical society. Mapping an event on web to the popularity score plays a pivot role to sense the social trends from the cyberspace. However, the complex semantic correspondence between texts and images, as well as the implicit text-image-popularity mapping mechanics pose a significant challenge to this non-trivial task. In this paper, we address this problem from a viewpoint of understanding the interpretable mapping mechanics. Concretely, we organize the keywords from different events into an unified graph. The unified graph facilitates to model the popularity of events via two-level mappings, i.e., the self excitation and the mutual excitation. The self-excitation assumes that each keyword forms the popularity while the mutual-excitation models that two keywords would excite each other to determine the popularity of an event. Specifically, we use Graph Neural Network (GNN) as the backbone to model the self-excitation, the mutual excitation and the context of images into a sparse and deep factor model. Besides, to our best knowledge, we release a challenge web event dataset for the popularity prediction task. The experimental results on three public datasets demonstrate that our method achieves significant improvements and outperforms the state-of-the-art methods. Dataset is publicly available at: https://github.com/pangjunbiao/Hot-events-dataset.