Identifying vital nodes from local and global perspectives in complex networks

Recognition of vital nodes in complex networks retains great importance in the improvement of network's robustness and vulnerability. Consistent research proposed various approaches like local-structure-based methods, e.g., degree centrality, pagerank, etc., and global-structure-based methods, e.g., betweenness, closeness centrality, etc., to evaluate the concerned nodes. Though their performance is amazingly well, these methods have undergone some intrinsic limitations. For instance, local-structure-based methods lose some sort of global information and global-structure-based methods are too complicated to measure the important nodes, particularly in networks where sizes become large. To tackle these challenges, we propose a Local-and-Global Centrality (LGC) measuring algorithm to identify the vital nodes through handling local as well as global topological aspects of a network simultaneously. In order to assess the performance of the proposed algorithm with respect to the state-of-the-art methodologies, we performed experiments through LCG, Betweenness (BNC), Closeness (CNC), Gravity (GIC), Page-Rank (PRC), Eigenvector (EVC), Global and Local Structure (GLS), Global Structure Model (GSM), and Profit-leader (PLC) methods on differently sized real-world networks. Our experiments disclose that LGC outperformed many of the compared techniques.

Automated summary

Recognition of vital nodes in complex networks is crucial for improving network robustness and vulnerability. Existing methods have limitations, leading to the proposal of a Local-and-Global Centrality (LGC) algorithm to address both local and global aspects of network topology simultaneously. Experiments show that LGC outperforms many state-of-the-art techniques in identifying vital nodes.