Finding Core-Periphery Structures With Node Influences

Detecting core-periphery structures is one of the outstanding issues in complex network analysis. Various algorithms can identify core nodes and periphery nodes. Recent advances found that many networks from real-world data be better modeled with multiple pairs In this study, we propose to use an influence propagation process detect framework, assume each node emit a certain amount propagate it through network. Then large influences as nodes, utilize maximum chain construct node-pairing determine pairs. This approach take interactions into consideration reduce noises finding Experiments on randomly generated confirm efficiency accuracy our algorithm.