The Short-Range Interaction Centrality (SRIC) index, originally called the key-borrower index (KBI), is a power index based on the concept of individual and group influence of nodes in a network [2, 3, 4]. Each node \(i\) is assumed to have an individual threshold of influence \(q_i\), which represents the level at which this node becomes affected. This threshold can be specified externally based on domain knowledge or determined from the network structure, for instance, as a function of the degree of each node. A group of nodes \(\Omega(i) \subset \mathcal{N}\) is called critical for node \(i\) if their collective influence exceeds the threshold \(q_i\), i.e.,
\begin{equation*}
\sum_{k \in \Omega(i)} a_{ki} \geq q_i.
\end{equation*}
A node \(k\) is termed pivotal for the group \(\Omega(i)\) if its removal renders the group non-critical. The set of pivotal members of \(\Omega(i)\) is denoted by \(\Omega^{p}(i)\).
The SRIC index considers only direct and indirect influence through one intermediate node, i.e., short-range influence. Formally, the initial adjacency matrix \(A\) is transformed into a matrix of direct influence \(P\), where \(D\) is the out-degree matrix. The indirect influence \(p_{ihj}\) of node \(i\) on node \(j\) via an intermediate node \(h\) is defined as
\begin{equation*}
p_{ihj} =
\begin{cases}
\frac{\min(a_{ih}, a_{hj})}{D_{kk}}, & \text{if } a_{ih} > 0, \ a_{hj} > 0, \ i \neq j \neq h, \\
0, & \text{otherwise}.
\end{cases}
\end{equation*}
The SRIC centrality of node \(i\) is defined as the average of the normalized short-range influence \(χ_i(j)\) of node \(i\) on all nodes \(j \in \mathcal{N}\). This is evaluated by considering all critical groups \(\Omega_k(j)\) in which node \(i\) is pivotal:
\begin{equation*}
c_{SRIC}(i) = \frac{1}{N} \sum_{j \in \mathcal{N}} \frac{χ_i(j)}{\sum_{h \in \mathcal{N}} χ_h(j)},
\end{equation*}
where
\begin{equation*}
χ_i(j) = \sum_{k: i \in \Omega^{p}_k(j)} \frac{p_{ij} + \sum_{h \in \Omega_k(j)} p_{ihj}}{|\Omega_k(j)|}.
\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] Aleskerov, F., Andrievskaya, I. and Permjakova, E. (2014), Key Borrowers Detected by the Intensities of Their Short-Range Interactions (2014). Higher School of Economics Research Paper No. WP BRP 33/FE/2014. doi: 10.2139/ssrn.2479272.
[3] Aleskerov, F. , Andrievskaya, I. , Nikitina A. and Shvydun, S. (2020). Key Borrowers Detected by the Intensities of Their Interactions. Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes), 355-389 World Scientific: Singapore Volume 1, Chapter 9. doi: 10.1142/9789811202391\_0009.
[4] Aleskerov, F., Shvydun, S. and Meshcheryakova, N. (2021). New centrality measures in networks: how to take into account the parameters of the nodes and group influence of nodes to nodes (1st ed.). Chapman and Hall/CRC. doi: 10.1201/9781003203421.