Identifying the node spreading influence with largest k-core values

• We present an improved neighbors' k-core (INK) method to evaluate the node spreading influence with largest k-core values.
• The INK method could identify the node spreading influence with largest k-core values more accurately.
• Kendall's tau τ   of INK method with α=1α=1 are highly identical to rank the node influence.

Identifying the nodes with largest spreading influence of complex networks is one of the most promising domains. By taking into account the neighbors' k-core values, we present an improved neighbors' k-core (INK) method which is the sum of the neighbors' k-core values with a tunable parameter α to evaluate the node spreading influence with largest k-core values. Comparing with the Susceptible–Infected–Recovered (SIR) results for four real networks, the INK method could identify the node spreading influence with largest k-core values more accurately than the ones generated by the degree k, closeness C, betweenness B and coreness centrality method.