Print Email Facebook Twitter Predicting nodal influence via local iterative metrics Title Predicting nodal influence via local iterative metrics Author Zhang, S. (TU Delft Multimedia Computing) Hanjalic, A. (TU Delft Intelligent Systems) Wang, H. (TU Delft Multimedia Computing) Department Intelligent Systems Date 2024 Abstract Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders (K∼4) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with K=4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range [91%,106%] across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark. Subject OA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:37a7080d-b540-4cfd-bc75-9a1315aa8c60 DOI https://doi.org/10.1038/s41598-024-55547-y ISSN 2045-2322 Source Scientific Reports, 14 (1) Part of collection Institutional Repository Document type journal article Rights © 2024 S. Zhang, A. Hanjalic, H. Wang Files PDF s41598-024-55547-y.pdf 4.41 MB Close viewer /islandora/object/uuid:37a7080d-b540-4cfd-bc75-9a1315aa8c60/datastream/OBJ/view