RESUMEN
Identification of the most influential spreaders that maximize information propagation in social networks is a classic optimization problem, called the influence maximization (IM) problem. A reasonable diffusion model that can accurately simulate information propagation in social networks is the key step to efficiently solving the IM problem. Synergism of neighbor nodes plays an important role in information propagation dynamics. Some known diffusion models have considered the reinforcement mechanism in defining the activation threshold. Most of these models focus on the synergetic effects of nodes on their common neighbors, but the accumulation of synergism has been neglected in previous studies. Inspired by these facts, we first discuss the catalytic role of synergism in the spreading dynamics of social networks and then propose a novel diffusion model called the synergism-based three-step cascade model (TSSCM) based on the above analysis and the three-degree influence theory. Finally, we devise an algorithm for solving the IM problem based on the TSSCM. Experiments on five real large-scale social networks demonstrate the efficacy of our method, which achieves competitive results in terms of influence spreading compared to the four other algorithms tested.
Asunto(s)
Difusión de la Información/métodos , Modelos Teóricos , Red Social , Algoritmos , Heurística Computacional , Simulación por Computador , HumanosRESUMEN
Click-through rate prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently to the prediction. We utilize stack autoencoder to explore high-order feature interactions and use improved FM for low-order feature interactions to portray the nonlinear associated relationship of features. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.
Asunto(s)
Publicidad , Internet , Aprendizaje Automático , Redes Neurales de la Computación , Algoritmos , Área Bajo la Curva , Atención , Humanos , Informática , Modelos Estadísticos , Reproducibilidad de los ResultadosRESUMEN
OBJECTIVE: To observe the activities of baicalin, berberine and Astragalus polysaccharides and their combinative effects on aldose reductase (AR) by a screening model of aldose reductase inhibitor (ARI) in vitro. METHODS: The inhibition of AR by baicalin, berberine and Astragalus polysaccharides and positive drug (Epalrestat) in different concentrations were evaluated, and their combinative effects were studied according to orthogonal t design. RESULTS: Baicalin and berberine had remarkable inhibitory effects on AR, the inhibitory rates were (88.4 +/- 7.4)% and (69.0 +/- 9.4)% at the concentration of 300 microg/mL. However, the combinative effect of the inhibition on AR by the two compounds was antagonistic action. Astragalus polysaccharides had no activity of inhibition on AR. CONCLUSION: Baicalin and berberine are the potential AR inhibitors as they can inhibit the activity of AR in vitro.