论文标题
时间网络中重要节点识别的时间重力模型
Temporal Gravity Model for Important Nodes Identification in Temporal Networks
论文作者
论文摘要
识别重要节点是网络科学中的核心任务之一，这对于分析网络的结构和了解网络上的动态过程至关重要。大多数现实世界的系统都是随时间变化的，可以很好地表示为时间网络。在物理学中的经典重力模型的推动下，我们提出了一个时间重力模型，以识别时间网络中的有影响力的节点。重力模型中的两个关键元素是对象的质量和两个对象之间的距离。在时间重力模型中，我们将节点视为对象，基本节点特性，例如静态和时间属性，例如节点的质量。我们定义时间距离，即最快到达距离和最短距离，为我们模型中两个节点之间的距离。我们利用我们的模型以及重要节点识别的基线中心方法。十个现实世界数据集的实验结果表明，时间重力模型在量化节点结构影响方面的基线方法优于基线方法。此外，当我们将最短距离作为两个节点之间的距离时，我们的模型是强大的，并且与基线方法相比，在量化节点扩散影响方面表现最好。
Identifying important nodes is one of the central tasks in network science, which is crucial for analyzing the structure of a network and understanding the dynamical processes on a network. Most real-world systems are time-varying and can be well represented as temporal networks. Motivated by the classic gravity model in physics, we propose a temporal gravity model to identify influential nodes in temporal networks. Two critical elements in the gravity model are the masses of the objects and the distance between two objects. In the temporal gravity model, we treat nodes as the objects, basic node properties, such as static and temporal properties, as the nodes' masses. We define temporal distances, i.e., fastest arrival distance and temporal shortest distance, as the distance between two nodes in our model. We utilize our model as well as the baseline centrality methods on important nodes identification. Experimental results on ten real-world datasets show that the temporal gravity model outperforms the baseline methods in quantifying node structural influence. Moreover, when we use the temporal shortest distance as the distance between two nodes, our model is robust and performs the best in quantifying node spreading influence compared to the baseline methods.
