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Graph neural networks (GNNs) can be effectively applied to solve many real‐world problems across widely diverse fields. Their success is inseparable from the message‐passing mechanisms evolving over the years. However, current mechanisms treat all node features equally at the macro‐level (node‐level), and the optimal aggregation method has not yet been explored. In this paper, we propose a new GNN called Graph Decipher (GD), which transparentizes the message flows of node features from micro‐level (feature‐level) to global‐level and boosts the performance on node classification tasks. Besides, to reduce the computational burden caused by investigating message‐passing, only the relevant representative node attributes are extracted by graph feature filters, allowing calculations to be performed in a category‐oriented manner. Experiments on 10 node classification data sets show that GD achieves state‐of‐the‐art performance while imposing a substantially lower computational cost. Additionally, since GD has the ability to explore the representative node attributes by category, it can also be applied to imbalanced node classification on multiclass graph data sets. [ABSTRACT FROM AUTHOR]

Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single‐source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources. [ABSTRACT FROM AUTHOR]