Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks

• URL: http://arxiv.org/abs/2403.15257v1
• Date: Fri, 22 Mar 2024 14:57:27 GMT
• Title: Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks
• Authors: Fanrui Zhang, Jiawei Liu, Qiang Zhang, Xiaoling Zhu, Zheng-Jun Zha,
• Abstract summary: We propose a novel Hierarchical Information Enhancement Network (HIENet) for cascade prediction. Our approach integrates fundamental cascade sequence, user social graphs, and sub-cascade graph into a unified framework.
• Score: 51.54002032659713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding information cascades in networks is a fundamental issue in numerous applications. Current researches often sample cascade information into several independent paths or subgraphs to learn a simple cascade representation. However, these approaches fail to exploit the hierarchical semantic associations between different modalities, limiting their predictive performance. In this work, we propose a novel Hierarchical Information Enhancement Network (HIENet) for cascade prediction. Our approach integrates fundamental cascade sequence, user social graphs, and sub-cascade graph into a unified framework. Specifically, HIENet utilizes DeepWalk to sample cascades information into a series of sequences. It then gathers path information between users to extract the social relationships of propagators. Additionally, we employ a time-stamped graph convolutional network to aggregate sub-cascade graph information effectively. Ultimately, we introduce a Multi-modal Cascade Transformer to powerfully fuse these clues, providing a comprehensive understanding of cascading process. Extensive experiments have demonstrated the effectiveness of the proposed method.

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