DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-10-23 DOI:10.1089/big.2023.0131
Qi Ouyang, Hongchang Chen, Shuxin Liu, Liming Pu, Dongdong Ge, Ke Fan
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引用次数: 0

Abstract

Predicting propagation cascades is crucial for understanding information propagation in social networks. Existing methods always focus on structure or order of infected users in a single cascade sequence, ignoring the global dependencies of cascades and users, which is insufficient to characterize their dynamic interaction preferences. Moreover, existing methods are poor at addressing the problem of model robustness. To address these issues, we propose a predication model named DropMessage Hypergraph Attention Networks, which constructs a hypergraph based on the cascade sequence. Specifically, to dynamically obtain user preferences, we divide the diffusion hypergraph into multiple subgraphs according to the time stamps, develop hypergraph attention networks to explicitly learn complete interactions, and adopt a gated fusion strategy to connect them for user cascade prediction. In addition, a new drop immediately method DropMessage is added to increase the robustness of the model. Experimental results on three real-world datasets indicate that proposed model significantly outperforms the most advanced information propagation prediction model in both MAP@k and Hits@K metrics, and the experiment also proves that the model achieves more significant prediction performance than the existing model under data perturbation.

DMHANT:用于信息传播预测的 DropMessage 超图注意力网络。
预测传播级联对于理解社交网络中的信息传播至关重要。现有方法总是关注单个级联序列中受感染用户的结构或顺序,忽略了级联和用户之间的全局依赖关系,不足以描述他们的动态互动偏好。此外,现有方法在解决模型稳健性问题方面也存在不足。为了解决这些问题,我们提出了一种名为 "DropMessage 超图注意力网络 "的预测模型,该模型基于级联序列构建超图。具体来说,为了动态获取用户偏好,我们根据时间戳将扩散超图划分为多个子图,开发超图注意力网络来显式学习完整的交互,并采用门控融合策略将它们连接起来进行用户级联预测。此外,为了提高模型的鲁棒性,还增加了一种新的立即删除方法 DropMessage。在三个真实数据集上的实验结果表明,所提出的模型在 MAP@k 和 Hits@K 两个指标上都明显优于最先进的信息传播预测模型,实验还证明该模型在数据扰动下比现有模型取得了更显著的预测性能。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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