Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

Abstract

Identifying influential spreaders in complex networks is crucial for containing virus spread, accelerating information diffusion, and promoting new products. In this paper, inspired by the effect of leaders on social ties, we propose the most influential neighbors' -shell index that is the weighted sum of the products between -core values of itself and the node with the maximum -shell values. We apply the classical Susceptible-Infected-Recovered (SIR) model to verify the performance of our method. The experimental results on both real and artificial networks show that the proposed method can quantify the node influence more accurately than degree centrality, betweenness centrality, closeness centrality, and -shell decomposition method.

Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks

“…The results also show the permeability to relate to the corresponding network diameter -the longest of all the shortest paths in the network (Table 1) -which again would fit into the breakthrough concept for its experimental determination. For the new geometrical features detected, the significance of k-core of network (Tables 3 and 4) suggests an analogy between different (supposedly uncorrelated) phenomenonthe passage of flow in a porous matrix, the spreading of an epidemic disease, and the dissemination of information in a social network [57]. The inspection of other geometrical features and their detected statistical significance on PE should signify the existence of some influential spreader nodes within the pore-throat network, for the passage of flow, which can easily be identified given their geometrical scores.…”

Power Grid‐Oriented Cascading Failure Vulnerability Identifying Method Based on Wireless Sensors

“…Under intentional attacks, important nodes in the network will be attacked. Important nodes are often measured by the node's betweenness centrality, degree [33], k-shell [34] value, etc. Therefore, we study three attacks that sort these three metrics in descending order to attack.…”

Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19

“…Superspreaders are of particular relevance, as they can influence the spread to a great extent. To identify such influential network nodes, epidemic simulations are used to test the performance of different measures [50,51] and to better understand social contagion processes in complex networks [52]. While the already mentioned measures of centrality cannot accurately predict individually relevant nodes, simulations with meta-centralities show more promising results [53].…”