The flow coefficient is a measure of local centrality introduced by Honey et al. [2] to quantify the capacity of a node to mediate information flow among its immediate neighbors. For a node \(i\), the flow coefficient \(c_{\mathrm{fc}}(i)\) is defined as the fraction of all possible two-step paths between pairs of its neighbors that actually pass through node \(i\):
\begin{equation*}
c_{\mathrm{fc}}(i) =
\frac{\sum_{j \neq k \in \mathcal{N}(i)} (A^2)_{jk}}
{|\mathcal{N}(i)| \, (|\mathcal{N}(i)| - 1)},
\end{equation*}
where \(\mathcal{N}(i)\) denotes the set of neighbors of node \(i\) and \(A\) is the adjacency matrix of the network. The term \((A^2)_{jk}\) counts the number of paths of length two between nodes \(j\) and \(k\).
High values of \(c_{\mathrm{fc}}(i)\) indicate that node \(i\) plays an important role in facilitating information flow among its neighbors, whereas low values suggest that the node’s neighbors are more directly connected to one another.

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] Honey, C. J., Kötter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences, 104(24), 10240-10245. doi: 10.1073/pnas.0701519104.