Cascade-based Adversarial Optimization for Influence Prediction

Bei Yuan, Meiling Li, Jie Chen, Shu Zhao, Yanping Zhang, Fulan Qian
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Abstract

Influence prediction methods based on specific diffusion models are not suitable for information spread in real social networks. In addition, influence prediction methods based on information such as structure graphs and text content are difficult to promote due to limitations in information acquisition. Aiming at the two problems faced in the research of influence prediction, the sequential and non-sequential dependencies in the information diffusion process are captured through the information cascades time-series information, and the Multi-Dependency Diffusion Attention Neural (MDDAN) network is proposed. The sequential dependency and non-sequential dependency of cascades are obtained through recurrent neural networks and attention mechanisms, respectively. At the same time, the model captures the user’s dynamic preferences through the attention mechanism because the information has a time decay characteristic. To reduce the noise interference in the information diffusion, a Cascade-based Adversarial Optimization (CAO) strategy is proposed. To prove that this strategy effectively enhances the generalization ability of cascade-based influence prediction models, we apply it to MDDAN and propose an Adversarial Multi-Dependency Diffusion Attention Neural (AMDDAN) network. Experiments on three real social network datasets show that MDDAN outperforms state-of-the-art cascade prediction models, and the addition of adversarial perturbation to AMDDAN improves the robustness of MDDAN.
基于级联的影响预测对抗优化
基于特定扩散模型的影响预测方法不适用于真实社会网络中的信息传播。此外,基于结构图、文本内容等信息的影响力预测方法由于信息获取的限制,难以推广。针对影响预测研究中面临的两个问题,通过信息级联时间序列信息捕获信息扩散过程中的顺序和非顺序依赖关系,提出了多依赖扩散注意神经网络(MDDAN)。通过递归神经网络和注意机制分别获得级联的顺序依赖和非顺序依赖。同时,由于信息具有时间衰减特性,该模型通过注意机制捕获用户的动态偏好。为了降低信息扩散过程中的噪声干扰,提出了一种基于级联的对抗优化策略。为了证明该策略有效地提高了基于级联的影响预测模型的泛化能力,我们将其应用于MDDAN,并提出了一种对抗多依赖扩散注意神经网络(AMDDAN)。在三个真实社会网络数据集上的实验表明,MDDAN优于最先进的级联预测模型,并且在AMDDAN中加入对抗扰动提高了MDDAN的鲁棒性。
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