PersonalityGate: A General Plug-and-Play GNN Gate to Enhance Cascade Prediction with Personality Recognition Task

TL;DR

This paper introduces PersonalityGate, a versatile GNN module that improves cascade prediction accuracy by incorporating personality recognition, demonstrating effectiveness on real-world social network datasets.

Contribution

The paper presents a novel plug-and-play GNN gate that jointly enhances cascade prediction and personality trait recognition within a unified framework.

Findings

PersonalityGate improves cascade prediction accuracy.

The framework successfully predicts individual personality traits.

Experimental results validate the effectiveness on real-world datasets.

Abstract

Cascade prediction estimates the size or the state of a cascade from either microscope or macroscope. It is of paramount importance for understanding the information diffusion process such as the spread of rumors and the propagation of new technologies in social networks. Recently, instead of extracting hand-crafted features or embedding cascade sequences into feature vectors for cascade prediction, graph neural networks (GNNs) are introduced to utilize the network structure which governs the cascade effect. However, these models do not take into account social factors such as personality traits which drive human's participation in the information diffusion process. In this work, we propose a novel multitask framework for enhancing cascade prediction with a personality recognition task. Specially, we design a general plug-and-play GNN gate, named PersonalityGate, to couple into existing GNN-based cascade prediction models to enhance their effectiveness and extract individuals' personality traits jointly. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed framework in enhancing GNN-based cascade prediction models and in predicting individuals' personality traits as well.