Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes' spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.Entropy 2020, 22, 242 2 of 19 diffusion [9], and even detect essential proteins [10]. On the other hand, by removing some critical nodes, it can greatly reduce the connectivity of the network to restrain the outbreak of epidemics [11] or spreading of rumors [12].The ongoing COVID-19 epidemics is catching wide attention around the world. Every country is making enormous effort to control the virus spreading. By analyzing social networks, it would be easier for us to control epidemics spreading. In the last decades, propagation dynamics has always been an important research direction. Many mechanisms, such as epidemic spreading [13][14][15][16], rumor propagation [17,18], social sudden events spreading [19], and e-commercial advertisements, are all closely related to complex network dynamics. Early in 1760, Daniel Bernoulli studied smallpox vaccine by using ordinary differential equations, and gave the Bernoulli equations [20] , which is one of the earliest propagation dynamics models. Later, Hamer presented the mass-action principle [21,22] when studying the recurring epidemics of measles. A.G. McKendrick and W.O. Kermack formulated a famous modern mathematical epidemic model named the Susceptible-Infected-Recovered (SIR) compartmental model when studying the spreading pattern of the Black Death and the plague in 1906. SIR compartmental model successfully predicted the outbreak of several epidemics [23]. Harding et al. [24] followed the maximum entropy (MaxEnt) principle when simulating on the SIS model to study epidemics spreading on networks. Wang et ...
ObjectiveTo explore the feasibility of dual-source dual-energy computed tomography (DSDECT) for hepatic iron and fat separation in vivo.Materials and MethodsAll of the procedures in this study were approved by the Research Animal Resource Center of Shanghai Ruijin Hospital. Sixty rats that underwent DECT scanning were divided into the normal group, fatty liver group, liver iron group, and coexisting liver iron and fat group, according to Prussian blue and HE staining. The data for each group were reconstructed and post-processed by an iron-specific, three-material decomposition algorithm. The iron enhancement value and the virtual non-iron contrast value, which indicated overloaded liver iron and residual liver tissue, respectively, were measured. Spearman's correlation and one-way analysis of variance (ANOVA) were performed, respectively, to analyze statistically the correlations with the histopathological results and differences among groups.ResultsThe iron enhancement values were positively correlated with the iron pathology grading (r = 0.729, p<0.001). Virtual non-iron contrast (VNC) values were negatively correlated with the fat pathology grading (r = −0.642,p<0.0001). Different groups showed significantly different iron enhancement values and VNC values (F = 25.308,p<0.001; F = 10.911, p<0.001, respectively). Among the groups, significant differences in iron enhancement values were only observed between the iron-present and iron-absent groups, and differences in VNC values were only observed between the fat-present and fat-absent groups.ConclusionSeparation of hepatic iron and fat by dual energy material decomposition in vivo was feasible, even when they coexisted.
Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social recommendation frameworks to aggregate both social and user-item interaction information simultaneously. However, most existing methods neglect the social inconsistency problem, which intuitively suggests that social links are not necessarily consistent with the rating prediction process. Social inconsistency can be observed from both contextlevel and relation-level. Therefore, we intend to empower the GNN model with the ability to tackle the social inconsistency problem. We propose to sample consistent neighbors by relating sampling probability with consistency scores between neighbors. Besides, we employ the relation attention mechanism to assign consistent relations with high importance factors for aggregation. Experiments on two real-world datasets verify the model effectiveness.
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