Information diffusion may lead to behaviors related to information content.This paper considers the co-existence of information and behavior diffusion in social networks.The state of users is divided into six categori...Information diffusion may lead to behaviors related to information content.This paper considers the co-existence of information and behavior diffusion in social networks.The state of users is divided into six categories,and the rules and model of collaborative diffusion of information and behavior are established.The influence of different parameters and conditions on the proportions of behavior diffusion nodes and information diffusion ones is analyzed experimentally.The results show that the proportion of nodes taking action in uniform networks is higher than that in non-uniform networks.Although users are more likely to take actions related to information content after spreading or knowing information,the results show that it has little influence on the proportion of users taking action.The proportion is mainly affected by the probability that users who do not take action become ones who take.The greater the probability,the less the proportion of nodes who know information.In addition,compared with choosing the same node as the initial information and behavior diffusion node,choosing different nodes is more beneficial to the diffusion of behaviors related to information content.展开更多
In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable ...In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.展开更多
基金supported in part by the National Natural Science Foundation of China(No.61973121)the Natural Science Research Project of the Anhui Higher Education Institution(No.KJ2021A0640).
文摘Information diffusion may lead to behaviors related to information content.This paper considers the co-existence of information and behavior diffusion in social networks.The state of users is divided into six categories,and the rules and model of collaborative diffusion of information and behavior are established.The influence of different parameters and conditions on the proportions of behavior diffusion nodes and information diffusion ones is analyzed experimentally.The results show that the proportion of nodes taking action in uniform networks is higher than that in non-uniform networks.Although users are more likely to take actions related to information content after spreading or knowing information,the results show that it has little influence on the proportion of users taking action.The proportion is mainly affected by the probability that users who do not take action become ones who take.The greater the probability,the less the proportion of nodes who know information.In addition,compared with choosing the same node as the initial information and behavior diffusion node,choosing different nodes is more beneficial to the diffusion of behaviors related to information content.
基金Supported by Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems(20200103)Doctoral Research Start-Up Fund of Anhui University of Finance&Economics(85051)。
文摘In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.
