The development of social media has provided open and convenient platforms for people to express their opinions, which leads to rumors being circulated. Therefore, detecting rumors from massive information becomes particularly essential. Previous methods for rumor detection focused on mining features from content and propagation patterns but neglected the dynamic features with joint content and propagation pattern. In this paper, we propose a novel heterogeneous GCN-based method for dynamic rumor detection (HDGCN), mainly composed of a joint content and propagation module and an ODE-based dynamic module. The joint content and propagation module constructs a content-propagation heterogeneous graph to obtain rumor representations by mining and discovering the interaction between post content and propagation structures in the rumor propagation process. The ODE-based dynamic module leverages a GCN integrated with an ordinary differential system to explore dynamic features of heterogeneous graphs. To evaluate the performance of our proposed HDGCN model, we have conducted extensive experiments on two real-world datasets from Twitter. The results of our proposed model have outperformed the mainstream model.
With the development of mobile telecommunication network and the intelligent terminal technology, the mobile terminal has become one of the most important agency to access the Internet information. Since a large share of content on the Internet is designed targeting the PC users, moreover, newly customized networks for mobile terminal are showing a lack of consideration on the aspects such as velocity, capability, adaptation and etc. When users are using the mobile terminal to access the Internet resources, they are facing the problems such as latency low-response and nonfluency. Based on UE(user experience) we propose an improved model of loading and display through experiments, taking the loading rate and respondence into account.
Keywords-mobile terminal; single page web; webpage optimization; user visualI.
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