Predicting the popularity of online content on social network can bring considerable economic benefits to companies and marketers, and it has wide application in viral marketing, content recommendation, early warning of social unrest, etc. The diffusion process of online contents is often a complex combination of both social influence and homophily; however, existing works either only consider the social influence or homophily of early infected users and fail to model the joint effect of social influence and homophily when predicting future popularity. In this study, we aim to develop a framework to unify the social influence and homophily in popularity prediction. We use an unsupervised graph neural network framework to model nondirectional social homophily and integrate the attention mechanism with the graph neural network framework to learn the directional and heterogeneous social relationship for generating social influence representation. On the other hand, existing research studies often overlook the social group characteristics of early infected users, and we try to divide users into different social groups based on user interest and learn the social group representation from clusters. We integrate the social influence, homophily, and social group representation of early infected users to make popularity predictions. Experiments on real datasets show that the proposed method significantly improves the prediction accuracy compared with the latest methods, which confirms the importance of joint model social influence and homophily and shows that social group characteristic is an important predictor in the popularity prediction task.
Wireless sensor networks consist of two kinds of nodes: the anchor and the Agent. The anchor is equipped with special hardware to obtain precise location information and employed to derive the locations of Agents. Due to the resource-limited nature of single sensors, actively participating nodes should be kept to a proper number. Based on an investigation on the trade-off between the localization accuracy and the computation complexity of sensor nodes, we propose a distributed algorithm to select subsets of anchor nodes for localization and analyze this algorithm regarding the energy consumption of every node.
Modeling and predicting the information diffusion process on social platforms is a critical task in many real-world applications. Recent studies generally model the diffusion graph using graph neural networks to capture the implicit dependencies among users. However, existing studies construct the diffusion graph in a way which cannot fully describe the global dependencies of users due to their narrow definition of user relationship. Meanwhile, graph neural networks in these methods are not suitable for the social network scenario which has scarce node attributes. Therefore, we propose a novel diffusion graph construction method which can enhance relations among users and adopt a simplified graph convolutional operation which is suitable for diffusion prediction scenario. The learned user embedding in our model can effectively preserve the microscopic structure and the high-order proximities between users lies in both the social graph and diffusion graph. Experimental results on four real-world datasets show that the proposed model is superior to the most advanced information diffusion prediction methods.
Global navigation satellite system (GNSS)-like the Global Positioning System (GPS) and the future Chinese Beidou system-can deliver very good position estimates under optimum conditions. However, especially in critical positioning scenarios like urban canyons or indoor environments the performance loss would be very high or GNSS based positioning is even not possible. Based on the concept of Cooperative Positioning in acquiring real-time positioning information of mobile robots, GNSS Peer-to-Peer Cooperative Positioning (P2P-CP) technology is proposed to overcome the shortage of GNSS positioning. Terrestrial ranging and communication modular are equipped with GNSS receivers to construct real-time CP network. The terrestrial ranging and communication modular respectively used for distance measurement and communication between nearby GNSS receivers, distributed algorithms are applied to fuse pseudorange and neighbors nodes distance to calculate the nodes position. Current research results of GNSS CP show that this new positioning strategy gets equal or better precision with less time cost compared with Assisted GNSS (AGNSS).
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