  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.
