Abstract：Collective influence (CI) centrality is one of the latest achievements in the measurement of node influences, which is designed based on the locally tree-like network and regards the importance of nodes in the global connection as the representation of their influence. However, it ignores the neighborhood distribution of each node and the difference in the robustness of the local network structure. Therefore, based on CI centrality, the influencing factors in the local network topology such as the neighborhood robustness of the target node, the degree distribution of the l-order neighbors and the connection strength between clusters of the l-order neighborhood are analyzed, defined and quantified. Then, a more universal centrality measurement method called NewCI is proposed to evaluate the influence of nodes. The stability of its overall performance and its better effectiveness and accuracy over CI in node influence measurement are demonstrated by the network invulnerability experiments in six real complex network datasets. Considering the effectiveness, time complexity and execution efficiency, NewCI also has a greater advantage than other commonly used centrality method.
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