With the prevalence of social media platforms, accurately identifying the same users across different networks through network alignment has become crucial. Existing methods often struggle due to sparse or absent user-identifiable information (node attributes), highlighting the need for augmenting node attributes. However, research on attribute augmentation remains largely under-explored. In this study, we aim to design augmented attributes that enhance network alignment by reflecting three key structural C haracteristics: (C1) global structural characteristic, reflects the global network structure; (C2) seed-based structural characteristic, leverages cross-network structural information associated with seed nodes; (C3) multi-aspect structural characteristic, employs diverse structural relationship measures. To this end, we propose a novel approach for designing trustworthy Augmented Seed-baSed and multI-aspect STructurAl iNformaTion (ASSISTANT) attributes. To enhance alignment performance, we also present a learning module that utilizes a gate mechanism to select the most effective measure dynamically. Extensive experiments across various datasets demonstrate the following: 1) Our network alignment framework, which includes a gate mechanism module, significantly outperforms state-of-the-art methods in alignment accuracy; 2) other state-of-the-art methods using ASSISTANT attributes as input substantially boosts their own alignment accuracy; and 3) using only ASSISTANT attributes without any training process also leads to effective alignment, showcasing their high trustworthiness.

Traffic speed prediction is a crucial task for optimizing navigation systems and reducing traffic congestion. Although there have been efforts to improve the accuracy of speed prediction by incorporating auxiliary features, such as traffic flow, weather, and time, types of auxiliary features are limited and their detailed relationships with speed have not been explored yet. In our study, we present the individual spatio-temporal (IST) dependencies on flow and speed, and characterize three types of IST-dependencies with the flow-to-flow, speed-to-speed, and flow-to-speed graphs. Then, we propose Auxiliary feature-aided Attention Network (ARIAN), a novel approach to judiciously learning the degrees of IST-dependencies with the three graphs and predicting the future speed by leveraging various auxiliary features. Through comprehensive experiments using 3 real-world datasets, we validate the superiority of ARIAN over 10 state-of-the-art methods and the effectiveness of each auxiliary feature and each dependency learner in ARIAN.

Spatio-temporal models, which combine GNNs (Graph Neural Networks) and RNNs (Recurrent Neural Networks), have shown state-of-the-art accuracy in traffic speed prediction. However, we find that they consider the spatial and temporal dependencies between speeds separately in the two (i.e., space and time) dimensions, thereby unable to exploit the joint-dependencies of speeds in space and time. In this paper, with the evidence via preliminary analysis, we point out the importance of considering individual dependencies between two speeds from all possible points in space and time for accurate traffic speed prediction. Then, we propose an Individual Spatio-Temporal graph (IST-graph) that represents the Individual Spatio-Temporal dependencies (IST-dependencies) very effectively and a Spatio-Temporal Graph ATtention network (ST-GAT), a novel model to predict the future traffic speeds based on the IST-graph and the attention mechanism. The results from our extensive evaluation with five real-world datasets demonstrate (1) the effectiveness of the IST-graph in modeling traffic speed data, (2) the superiority of ST-GAT over 5 state-of-the-art models (i.e., 2-33% gains) in prediction accuracy, and (3) the robustness of our ST-GAT even in abnormal traffic situations.