Identifying important nodes in networks is essential to analysing their structure and understanding their dynamical processes. In addition, myriad real systems are time-varying and can be represented as temporal networks. Motivated by classic gravity in physics, we propose a temporal gravity model to identify important nodes in temporal networks. In gravity, the attraction between two objects depends on their masses and distance. For the temporal network, we treat basic node properties (e.g., static and temporal properties) as the mass and temporal characteristics (i.e., fastest arrival distance and temporal shortest distance) as the distance. Experimental results on 10 real datasets show that the temporal gravity model outperforms baseline methods in quantifying the structural influence of nodes. When using the temporal shortest distance as the distance between two nodes, the proposed model is more robust and more accurately determines the node spreading influence than baseline methods. Furthermore, when using the temporal information to quantify the mass of each node, we found that a novel robust metric can be used to accurately determine the node influence regarding both network structure and information spreading.

识别网络中的重要节点对于分析其结构和了解其动态过程至关重要。另外，无数的真实系统是随时间变化的，可以表示为时间网络。受物理学经典引力的启发，我们提出了一种时间引力模型来识别时间网络中的重要节点。在重力作用下，两个物体之间的吸引力取决于它们的质量和距离。对于时间网络，我们将基本节点属性（例如，静态和时间属性）视为质量，将时间特征（即，最快到达距离和时间最短距离）视为距离。在10个真实数据集上的实验结果表明，在量化节点的结构影响方面，时间重力模型的性能优于基线方法。当使用时间最短距离作为两个节点之间的距离时，与基线方法相比，所提出的模型更加健壮，并且可以更准确地确定节点扩展的影响。此外，当使用时间信息量化每个节点的质量时，我们发现可以使用一种新颖的鲁棒度量来准确确定有关网络结构和信息传播的节点影响。