A novel higher-order neural network framework based on motifs attention for identifying critical nodes

Quantifying the importance of nodes is a fundamental and significant problem in network science. Potential applications including identifying critical people, epidemic spreading control, rumor control, protecting critical infrastructures that is vulnerable, predicting key proteins, and so on. However, most of the existing methods concentrate on the iterative approaches, only a minority of methods attempt to explore the importance of nodes by adopting machine learning. More importantly, in reality, multiple nodes often work together or generate associations. Although the existing important nodes mining methods based on machine learning consider network structures and node features, all of them ignore the higher-order relationships, i.e., multiple nodes interactions. Inspired by this, to accurately identify critical nodes with higher-order interaction information in networks, we propose a novel higher-order neural network framework based on motif-attention (i.e., HONNMA) from the perspective of higherorder interactions. The proposed framework (i.e., HONNMA) can encode the higher-order interaction relationships by employing weighted motif adjacency matrix, and learn the attention weights by motif-based attention mechanism, then adopt skip connection to obtain the node embeddings, finally use multiple feedforward layers to predict the critical scores of nodes. Extensive experiments conducted on four real-world datasets demonstrate the proposed model significantly outperforms the existing state-of-the-art baseline methods. To emphasize that, the higher-order neural network framework (i.e., HONNMA) can enhance the prediction of important nodes such as critical infrastructures, critical people, critical scientific publications, and critical proteins, and much more.(c) 2023 Elsevier B.V. All rights reserved.