Abstract
Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high‐order graph neural network (K‐GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.
Highlights
In the 5G communication network environment, the number of data transmission is increasing, and there are many types of data
In order to mine the difference between rumor and nonrumor implied layer structure features with better performance, we propose a two-layer graph convolutional attention network, which obtains the propagation and dispersion properties through two parts of top-down and bottom-up graph attention network (GAT), respectively [5]
This is because the goal of automated rumor detection is to save labor costs, nonrumors still account for a large proportion of text messages in the entire social network
Summary
In the 5G communication network environment, the number of data transmission is increasing, and there are many types of data. Different types of data storage methods are different. It is difficult to collect, store, analyze, and query big data. The commonly used big data analysis and collection technology cannot meet the development needs of all walks of life when applied. When the technology is improved and optimized, the appropriate data mining algorithm should be selected to extract the effective information. After the analysis of the mined big data, it should be presented to users in the form of visualization of data charts, and it should be evaluated quantitatively. In order to further improve the existing data mining technology, we can automatically extract relevant information from valid data through the artificial intelligence algorithm and semantic search engine design, so as to improve the ability of data collection and screening
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
