摘要
基于图的虚假评论检测主要面临着如何在仅有少量正样本标注的属性图中,有效聚合图中不同关系的邻居信息,提高图表示学习对于异常节点的敏感性和泛化能力的挑战。针对此挑战,提出基于元学习的多信息融合图差异网络(multi-information fusion graph deviation network based on meta-learning,MetaMGDN)。通过构建多视图划分与多信息融合模块,充分挖掘用户、项目、评分的结构信息与属性信息,以实现网络对多方面信息的获取并挖掘评论节点之间的关系。设计多视图邻域差异聚合模块,合并邻域信息与自身-邻域差异信息,使网络同时关注节点之间的关联性与差异性,提高了网络对于异常节点的敏感性。最后,引入元学习框架,利用多个辅助网络增强目标网络学习小样本任务的经验,从而在小样本虚假评论检测场景下保持较高泛化能力。在真实公开评论数据集上进行的实验表明,Meta-MGDN在基于图的虚假评论检测领域上的效果优于先进的基线。
Graph-based fake review detection primarily faces the notoriously challenge of effectively aggregating neighbor information from different relations particularly in an attributed graph with only a limited number of labeled positive samples.The goal is to enhance the sensitivity of graph representation learning to anomalous nodes and improve its generalization capability.To address this challenge,we propose a meta-learning-based multi-information fusion graph difference network(Meta-MGDN).By innovatively constructing a multi-view partitioning and multiinformation fusion module,the structural and attribute information of users,items,and rating relationships are fully explored to enable the network to capture multi-faceted information and uncover and analyze relationships between review nodes.We design a multi-view neighborhood difference aggregation module that integrates neighborhood information with self-neighborhood difference information,enabling the network to simultaneously capture both the correlation and the differences between nodes,thereby significantly enhancing its sensitivity to potentially anomalous review nodes.Finally,a meta-learning framework is introduced to simulate the few-shot problem using multiple auxiliary networks,which helps the network practically maintain a high generalization ability in the critical issue of few-shot fake review detection scenarios.Experiments on real-world datasets show that Meta-MGDN significantly outperforms the state-of-the-art baselines in the field of graph-based fake review detection.
作者
陈卓
狄家奇
张傲
卢晓鹏
杜军威
Chen Zhuo;Di Jiaqi;Zhang Ao;Lu Xiaopeng;Du Junwei(School of Data Science,Qingdao University of Science and Technology,Qingdao,Shandong 266061)
出处
《计算机研究与发展》
北大核心
2026年第2期393-404,共12页
Journal of Computer Research and Development
关键词
虚假评论检测
图神经网络
元学习
小样本
图异常检测
fake review detection
graph neural network
meta-learning
few-shot
graph anomaly detection
