A method based on k-shell decomposition to identify influential nodes in complex networks

Abstract

Finding the most influential nodes in complex networks is one of the open research issues. This problem can be divided into two sub-problems: (1) identifying the influential nodes and ranking them based on the individual influence of each node and (2) selecting a group of nodes to achieve maximum propagation in the network. In most of the previous articles, only one of these sub-issues has been considered. Therefore, this article presents a method to measure the spreading power of influential nodes in the network (the first sub-problem) and select the best group from them (the second sub-problem). In the proposed method, first, the input network is allocated to different communities. Then, the common neighbors and the degrees of the two end vertices of each edge are used to weigh the graph edges in each community. Next, in each of the communities, the nodes' propagation power is measured and ranked. Finally, a group of influential nodes is selected to start the propagation process.Eight data sets collected from real networks have been used for evaluation. The proposed method is compared with other previously known methods based on ranking accuracy, assigning different ranks to nodes, and calculating the amount of diffusion created in the network. The results show the proposed method's significant superiority over other methods in all test datasets.

Integrating local and global information to identify influential nodes in complex networks

“…A node with a higher Ks-value is more significant in the network and should be given more attention or consideration when interpreting the model or making choices based on its predictions. The Ks metric indicates that a cluster of nodes will exhibit comparable significance within a network 11 , yet it falls short in equitably distinguishing the nodes that possess greater influences.…”

“…In latest years, methodologies for locating prominent nodes have gotten more targeted, relying only on global or local data. For example, K-shell decomposition (Ks) 10 , 11 and the Degree Centrality (DC) 12 approach is two of the most thoroughly explored interpretations of global and local information, respectively. Because of their simplicity, these two techniques have achieved broad use in networks of all sizes.…”