Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

In various real-world applications such as weather forecasting, energy consumption planning, and traffic flow prediction, time serves as a critical variable. These applications can be collectively referred to as time-series prediction problems. Despite recent advancements with Transformer-based solutions yielding improved results, these solutions often struggle to capture the semantic dependencies in time-series data, resulting predominantly in temporal dependencies. This shortfall often hinders their ability to effectively capture long-term series patterns. In this research, we apply time-series decomposition to address this issue of long-term series forecasting. Our method involves implementing a time-series forecasting approach with deep series decomposition, which further decomposes the long-term trend components generated after the initial decomposition. This technique significantly enhances the forecasting accuracy of the model. For long-term time-series forecasting (LTSF), our proposed method exhibits commendable prediction accuracy on four publicly available datasets—Weather, Electricity, Traffic, ILI—when compared to prevailing methods. The code for our method is accessible at https://github.com/wangyang970508/LSTF_MD.