Identification of critical nodes by fusing propagation probabilities and entropy in binary networks

Identification of critical nodes is crucial for effectively allocating resources and prioritizing tasks in complex networks, which significantly enhances the stability and the efficiency of networks in real-world environments. Generally, existing studies primarily focus on extracting multiple different influential factors from network topology, but they have to face accuracy limitations due to high computational complexity, overlapping influence ranges, and information loss. Inspired by information entropy, in this paper, we explore to identify critical node in complex networks from the perspective of inter-node propagation probabilities. We introduce an innovative critical node ranking algorithm, named MNIE (Mixed Node Information Entropy). MNIE initially segments the node influence within the network topology by distinguishing between global and local effects so as to integrate a more comprehensive topological features set. Then, we refine the connection probability calculation and integrate the features derived from the network structural topology with the probabilities of information transmission (infection rates) among the nodes. Experimental results on 9 real-world networks and 4 synthetic datasets indicate that MNIE enhances the identification of critical nodes and accomplishes better than state-of-the-art methods on monotonicity and accuracy.