IC-SNI: measuring nodes' influential capability in complex networks through structural and neighboring information

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Nandi, S (Author) Other; Malta, MC (Author) FEUP; Maji, G (Author) Other; Dutta, A (Author) Other

Journal

Title: Knowledge and Information Systems

Vol. 67

Pages: 1309-1350

ISSN: 0219-1377

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-017-8CA

DOI: 10.1007/s10115-024-02262-9

Abstract (EN): Influential nodes are the important nodes that most efficiently control the propagation process throughout the network. Among various structural-based methods, degree centrality, k-shell decomposition, or their combination identify influential nodes with relatively low computational complexity, making them suitable for large-scale network analysis. However, these methods do not necessarily explore nodes' underlying structure and neighboring information, which poses a significant challenge for researchers in developing timely and efficient heuristics considering appropriate network characteristics. In this study, we propose a new method (IC-SNI) to measure the influential capability of the nodes. IC-SNI minimizes the loopholes of the local and global centrality and calculates the topological positional structure by considering the local and global contribution of the neighbors. Exploring the path structural information, we introduce two new measurements (connectivity strength and effective distance) to capture the structural properties among the neighboring nodes. Finally, the influential capability of a node is calculated by aggregating the structural and neighboring information of up to two-hop neighboring nodes. Evaluated on nine benchmark datasets, IC-SNI demonstrates superior performance with the highest average ranking correlation of 0.813 with the SIR simulator and a 34.1% improvement comparing state-of-the-art methods in identifying influential spreaders. The results show that IC-SNI efficiently identifies the influential spreaders in diverse real networks by accurately integrating structural and neighboring information.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 42