Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks

TL;DR

This paper introduces a novel learning framework combining GCNs and RNNs to identify critical nodes in temporal networks, improving prediction accuracy for spreading influence over time.

Contribution

It proposes a new method that leverages sequence topological information with GCNs and RNNs to better identify influential nodes in dynamic networks.

Findings

Outperforms traditional methods in accuracy

Effective on real-world temporal networks

Uses weighted SIR model for evaluation

Abstract

Many real-world systems can be expressed in temporal networks with nodes playing far different roles in structure and function and edges representing the relationships between nodes. Identifying critical nodes can help us control the spread of public opinions or epidemics, predict leading figures in academia, conduct advertisements for various commodities, and so on. However, it is rather difficult to identify critical nodes because the network structure changes over time in temporal networks. In this paper, considering the sequence topological information of temporal networks, a novel and effective learning framework based on the combination of special GCNs and RNNs is proposed to identify nodes with the best spreading ability. The effectiveness of the approach is evaluated by weighted Susceptible-Infected-Recovered model. Experimental results on four real-world temporal networks demonstrate that the proposed method outperforms both traditional and deep learning benchmark methods in terms of the Kendall $\tau$ coefficient and top $k$ hit rate.