Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.
Network approaches have been widely accepted to guide surgical strategy and predict outcome for epilepsy treatment.This study starts with a single oscillator to explore brain activity,using a phenomenological model capable of describing healthy and epileptic states.The ictal number of seizures decreases or remains unchanged with increasing the speed of oscillator excitability and in each seizure,there is an increasing tendency for ictal duration with respect to the speed.The underlying reason is that the strong excitability speed is conducive to reduce transition behaviors between two attractor basins.Moreover,the selection of the optimal removal node is estimated by an indicator proposed in this study.Results show that when the indicator is less than the threshold,removing the driving node is more possible to reduce seizures significantly,while the indicator exceeds the threshold,the epileptic node could be the removal one.Furthermore,the driving node is such a potential target that stimulating it is obviously effective in suppressing seizure-like activity compared to other nodes,and the propensity of seizures can be reduced 60%with the increased stimulus strength.Our results could provide new therapeutic ideas for epilepsy surgery and neuromodulation.
