This paper introduces a new centrality measure called subgraph centrality (SC), which quantifies the participation of each node in all subgraphs of a network. Unlike traditional measures such as degree, betweenness, closeness, and eigenvector centrality, SC gives more weight to smaller subgraphs, making it particularly suitable for identifying network motifs. SC is derived mathematically from the spectra of the adjacency matrix of the network and has been shown to be more effective at distinguishing nodes in real-world networks. The authors tested SC on eight real-world networks and found that it provides clear rankings of nodes and exhibits scale-free characteristics. In the context of protein interaction networks, SC was found to be more correlated with the lethality of proteins than the number of interactions per protein. The paper also compares SC with other centrality measures, showing that it has greater discriminative power. SC is defined as the sum of weighted closed walks starting and ending at a node, with smaller walks contributing more to the centrality. The authors prove that SC can be calculated using the eigenvalues of the adjacency matrix. They also show that SC is more effective than other centrality measures in distinguishing nodes in regular graphs and real-world networks. The paper concludes that SC is a useful measure for analyzing complex networks and provides insights into the structural properties of networks.This paper introduces a new centrality measure called subgraph centrality (SC), which quantifies the participation of each node in all subgraphs of a network. Unlike traditional measures such as degree, betweenness, closeness, and eigenvector centrality, SC gives more weight to smaller subgraphs, making it particularly suitable for identifying network motifs. SC is derived mathematically from the spectra of the adjacency matrix of the network and has been shown to be more effective at distinguishing nodes in real-world networks. The authors tested SC on eight real-world networks and found that it provides clear rankings of nodes and exhibits scale-free characteristics. In the context of protein interaction networks, SC was found to be more correlated with the lethality of proteins than the number of interactions per protein. The paper also compares SC with other centrality measures, showing that it has greater discriminative power. SC is defined as the sum of weighted closed walks starting and ending at a node, with smaller walks contributing more to the centrality. The authors prove that SC can be calculated using the eigenvalues of the adjacency matrix. They also show that SC is more effective than other centrality measures in distinguishing nodes in regular graphs and real-world networks. The paper concludes that SC is a useful measure for analyzing complex networks and provides insights into the structural properties of networks.