Identification of Key Actor Nodes: A Centrality Measure Ranking Aggregation Approach

The identification of key actors in complex networks has gathered significant interest by virtue of their importance in modern applications. Several of the existing methods employ standard centrality measures to achieve their goal and as a result, one of the main challenges is identifying key actor nodes with high relevance across all such measures. In this work, we propose a model based on the use of graph convolutional networks (GCNs) that retrieves the key actors in a network based on a centrality measure ranking aggregation scheme. We experimentally demonstrate the effectiveness of our solution compared to baseline and state-of-the-art approaches in terms of: i) accuracy, ii) performance compared to standard machine learning approaches, and iii) influence propagation capabilities.