During the past years, network dynamics has been widely investigated in various disciplines. As a practical and convenient description for social networks, signed networks have also garnered significant attention. In this work, we study information spreading with relative attributes on signed networks, where edges are assigned positive or negative labels, describing friendly or hostile relationships. We define the attribute of information by a degree that can be either ‘good’ or ‘bad’ and assume that the spreading willingness of the information receiver depends on not only its relation with others but also the attribute of information. A pair-wise potential relation identification algorithm is designed based on the shortest path approach and structural balance theory. Both simulations on randomly signed networks and empirical experiments on real datasets show that the proposed information spreading could be approximately investigated within a local 2-order neighborhood. In addition, the ratio of potential friendly nodes with a target node is consist with network content. Finally, the propagation speed of ‘good’ information would unexpectedly slow down when the ratio of positive edges is larger than an estimated threshold. The presented model could be referred to in real social scenarios, such as product promotion, advertisement media, and rumor mongering.

在过去的几年中，网络动力学已在各个学科中得到广泛研究。作为对社交网络的一种实用方便的描述，签名网络也引起了极大的关注。在这项工作中，我们研究在签名网络上具有相对属性的信息传播，其中对边缘分配了正向或负向标签，描述了友好或敌对的关系。我们通过“好”或“坏”的程度来定义信息的属性，并假设信息接收者的传播意愿不仅取决于其与他人的关系，还取决于信息的属性。基于最短路径法和结构平衡理论设计了成对的电位关系识别算法。在随机签名网络上的模拟以及在真实数据集上的经验实验都表明，可以在本地2阶邻域内对提议的信息传播进行近似研究。另外，潜在友好节点与目标节点的比率由网络内容组成。最后，当上升沿的比率大于估计的阈值时，“良好”信息的传播速度会意外降低。可以在真实的社会场景中引用所提出的模型，例如产品促销，广告媒体和谣言传播。当上升沿的比率大于估计的阈值时，“良好”信息的传播速度会意外降低。可以在真实的社会场景中引用所提出的模型，例如产品促销，广告媒体和谣言传播。当上升沿的比率大于估计的阈值时，“良好”信息的传播速度会意外降低。可以在真实的社会场景中引用所提出的模型，例如产品促销，广告媒体和谣言传播。