Ranking influential nodes in networks from aggregate local information

Bartolucci, Silvia; Caccioli, Fabio; Caravelli, Francesco; Vivo, Pierpaolo

doi:10.1103/physrevresearch.5.033123

Cited by 10 publications

(3 citation statements)

References 76 publications

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“…The example suggests that the number of 2-, 3- and 4-hop walks possibly reflect nodal spreading influence better than the global metric (eigenvector centrality). Furthermore, it has been observed and partially proved in previous work that a centrality metric like betweenness with a high computational complexity is correlated with local metrics derived from a low order neighborhood 18 , 30 . Hence, global network information, i.e., large K , is not necessarily needed in nodal influence prediction.…”

Section: Introductionmentioning

confidence: 80%

Predicting nodal influence via local iterative metrics

Zhang,

Hanjalic,

Wang

2024

Sci Rep

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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 ($$K\sim 4$$ 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 $$K=4$$ K = 4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range $$[91\%,106\%]$$ [ 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.

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Section: Introductionmentioning

confidence: 80%

Predicting nodal influence via local iterative metrics

Zhang,

Hanjalic,

Wang

2024

Sci Rep

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“…Another example of an indicator used in supply chain reconstruction research is Page Rank (or Bonacich centrality, or the 'influence vector') [23,24], which is a classic node-level metric measuring the centrality of a node. In that sense, it is a topological indicator.…”

Section: Evaluating the Reconstructed Networkmentioning

confidence: 99%

“… Bartolucci et al [23] show that 'upstreamness' , a classic metric in I-O economics, can be recovered very well from networks reconstructed from maximum entropy, as long as the networks are not too sparse. This is because, under very general conditions for the original network, the first-order approximation of a node's upstreamness is its upstreamness in the maximum entropy-reconstructed network[68].…”

mentioning

confidence: 99%

Reconstructing supply networks

Mungo,

Brintrup,

Garlaschelli

et al. 2024

J. Phys. Complex.

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Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda.

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