Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

中文翻译：

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通过符号图互信息最大化的关系表示学习进行信任预测

信任预测对于增强可靠性和降低来自不可靠节点的风险至关重要，尤其是对于开放网络环境中的在线应用程序而言。信任预测中的一个基本事实是准确地测量两个交互实体的关系。然而，大多数现有方法推断交互实体之间的信任关系通常依赖于对图上节点之间的相似性建模，而忽略语义关系和否定链接的影响（例如，不信任关系）。在本文中，我们提出了一种通过有符号图互信息最大化（SGMIM）进行关系表示学习的方法。在SGMIM中，我们结合了翻译模型和积极的点式互信息来增强关系表示，并采用互信息最大化来对齐实体和关系语义空间。此外，我们进一步开发了用于进行准确信任预测的符号预测模型。我们基于所学的关系表示，在信任网络中进行链路符号预测。在有关信任预测任务的四个真实世界数据集中的大量实验结果表明，SGMIM明显优于最新的基线方法。