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Chen et al. (2020) introduced the concept of measuring and relieving the over-smoothing problem for graph neural networks from a topological view. Fei (2018) proposed a novel approach to identifying influential nodes in complex networks, which also summarizes existing centrality measures and provides a means to capture the intensity and mutual attraction between nodes on a distributed network. Kim et al. (2018) focused on the volume of nodes, ignoring the individual effect of more influential nodes on the network. These authors provide a means to analyze the comparative power or effect that each system maintains, allowing researchers to isolate the central systems and network factors that form giant-node components within complex networks.