HybridRank is a hybrid centrality measure designed to identify a set of influential spreaders in a network by combining topological properties of nodes [2]. The method operates in two main steps: computing a hybrid centrality score for each node and selecting a subset of influential spreaders.
The hybrid centrality \( c_{HC}(i) \) of node \( i \) is defined as
\begin{equation*}
c_{HC}(i) = c_{EV}(i) \sum_{j \in \mathcal{N}(i)} k_s(j),
\end{equation*}
where \( c_{EV}(i) \) denotes the eigenvector centrality of node \( i \), \( k_s(j) \) is the \(k\)-shell index of neighbor \( j \), and \( \mathcal{N}(i) \) represents the set of neighbors of \( i \).
In the second step, nodes are ranked according to their hybrid centrality scores. The algorithm then iteratively selects the node with the highest \( c_{HC} \) value as a seed and removes it along with its immediate neighbors from consideration. This process continues until the desired number of non-adjacent influential spreaders is obtained.

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] Ahajjam, S., & Badir, H. (2018). Identification of influential spreaders in complex networks using HybridRank algorithm. Scientific reports, 8(1), 11932. doi: 10.1038/s41598-018-30310-2.