RA-ICM：一种融合用户关系与态度的新型独立级联模型
RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes
AI总结：
针对现有信息传播模型忽略非相邻用户关系与用户态度的问题，提出融合两类因素的RA-ICM模型，经6个真实微博数据集验证，其预测精度更高、时间复杂度更低，更贴合真实传播规律。
摘要：
社交网络的快速发展产生了广泛的社会影响，为社会问题研究提供了便利。准确预测社交网络中的信息传播过程，对于及时把握事件走向、以科学方式有效应对社会问题至关重要。非相邻用户之间的关系以及用户态度会对社交网络内的信息传播过程产生显著影响。然而，现有研究忽略了这两个要素，给准确预测信息传播过程带来了挑战。这一局限性严重阻碍了社交网络中情绪传染与影响最大化相关研究的推进。为解决上述问题，我们通过纳入非相邻用户关系与用户态度的影响，在独立级联模型的基础上提出了一种新型信息传播模型。在6个真实Weibo数据集上得到的实验结果验证了所提模型的有效性，具体表现为预测精度提升、时间复杂度降低。此外，该模型预测的社交网络信息传播趋势与实际信息传播过程高度吻合，彰显了其优越性。
英文摘要：
The rapid development of social networks has a wide range of social effects, which facilitates the study of social issues. Accurately forecasting the information propagation process within social networks is crucial for promptly understanding the event direction and effectively addressing social problems in a scientific manner. The relationships between non-adjacent users and the attitudes of users significantly influence the information propagation process within social networks. However, existing research has ignored these two elements, which poses challenges for accurately predicting the information propagation process. This limitation significantly hinders the study of emotional contagion and influence maximization in social networks. To address these issues, by considering the relationships between non-adjacent users and the influence of user attitudes, we propose a new information propagation model based on the independent cascade model. Experimental results obtained from six real Weibo datasets validate the effectiveness of the proposed model, which is reflected in increased prediction accuracy and reduced time complexity. Furthermore, the information dissemination trend in social networks predicted by the proposed model closely resembles the actual information propagation process, which demonstrates the superiority of the proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
