Temporal Gravity Model for Important Nodes Identification in Temporal Networks

TL;DR

This paper introduces a novel temporal gravity model inspired by physics to identify influential nodes in time-varying networks, outperforming baseline methods in real-world datasets.

Contribution

The paper proposes a new temporal gravity model that incorporates temporal distances and node properties to better identify influential nodes in dynamic networks.

Findings

The model outperforms baseline methods in real-world datasets.

Using temporal shortest distance enhances robustness and influence quantification.

The approach effectively captures node importance in dynamic network structures.

Abstract

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.