The maximal clique centrality (MCC) is based on the observation that essential proteins in a yeast protein-protein interaction network tend to be highly clustered [2]. A maximal clique is a fully connected subgraph that is not contained in any larger fully connected subgraph. Let \(S_i\) denote the set of maximal cliques containing node \(i\). The MCC of node \(i\) is then defined as
\begin{equation*}
c_{\mathrm{MCC}}(i) = \sum_{C \in S_i} (|C|-1)!,
\end{equation*}
where \(|C|\) is the size of clique \(C\). Under this definition, the MCC of an isolated node is \(1\). For a node \(i\) whose neighbors are all disconnected (i.e., there is no edge between any two neighbors of node \(i\)), the MCC reduces to the degree of node \(i\):
\begin{equation*}
c_{\mathrm{MCC}}(i) = \sum_{j=1}^{N} a_{ij} = d_i.
\end{equation*}

References

[1] Shvydun, S. (2025). Zoo of Centralities: Encyclopedia of Node Metrics in Complex Networks. arXiv: 2511.05122 https://doi.org/10.48550/arXiv.2511.05122
[2] Chin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., & Lin, C. Y. (2014). cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology, 8(Suppl 4), S11. doi: 10.1186/1752-0509-8-S4-S11.