Improved collective influence of finding most influential nodes based on disjoint-set reinsertion

Identifying vital nodes in complex networks is a critical problem in the field of network theory. To this end, the Collective. Influence (CI) algorithm has been introduced and shows high efficiency and scalability in searching for the influential nodes in. the optimal percolation model. However, the crucial part of the CI algorithm, reinsertion, has not been significantly investigated. or improved upon. In this paper, the author improves the CI algorithm and proposes a new algorithm called Collective-Influence-Disjoint-Set-Reinsertion (CI_DR) based on disjoint-set reinsertion. Experimental results on 8 datasets with scales of a million nodes and 4 random graph networks demonstrate that the proposed CI_DR algorithm outperforms other algorithms, including Betweenness centrality, Closeness centrality, PageRank centrality, Degree centrality (HDA), Eigenvector centrality, Nonbacktracking centrality and Collective Influence with original reinsertion, in terms of the Robustness metric. Moreover, CI_DR is applied to an international competition on optimal percolation and ultimately ranks in 7th place.