【摘要】 现有基于图的方法未区分用户间的异配、同配关系，造成用户表示中存在异配边带来的噪声，弱化了社交机器人与人类用户之间的差异性，检测准确率降低；社交机器人持续演化模拟人类用户的关注数、推文发布数等元数据，容易导致较难识别的仿真社交机器人比例增加，检测召回率降低.提出一种利用异配及同配关系的社交机器人检测方法，通过记忆网络构建异配、同配关系原型来识别用户关系类型，减少异配边对用户表示的干扰，增加不同类型用户的特征区分性；在损失函数中引入调节因子，提高较难分类用户在模型参数更新过程中的损失贡献，增强了模型对仿真社交机器人的识别效果.实验结果表明，提出的方法优于当前先进方法，此方法通过区分用户间的异配和同配关系，降低异配边的权重，增强了用户表示的类别区分度，即使在低同配性分数的情况下也有效提升了检测准确率.更多还原

【Abstract】 Due to can not differentiate the heterophilic and homophilic relations between users, the existing-methods can lead to noise in user representations of heterophilic edges, diminishing the differences between social robots and human users, resulting in a decrease in detection accuracy. Social robots continuously evolve to mimic human user behaviors such as the number of followers and the number of tweets posted,leading to an increased proportion of sophisticated social robots, reducing the detection recall rate. In this paper, a social robot detection method was proposed based on both heterophilic and homophilic relations,-memory network for constructing prototypes of heterophilic and homophilic relations to identify user-types, reducing the interference of heterophilic edges on user representations and enhancing the feature distinctiveness between different types of users. A regulatory factor was introduced into loss function to enhance the loss contribution of hard-to-classify users during the parameter optimization process, improving the ability of model to recognize sophisticated social robots. Experimental results show that the proposed method can-current advanced approaches in distinguishing heterophilic and homophilic relations between users,-the weight of heterophilic edges, enhancing the class separability of user representations, and improving the detection accuracy effectively even in cases of low homophily scores.更多还原