The surge in blockchain transaction volume has corresponds with a rise in phishing incidents. Despite the widespread use of graph representation learning in fraud detection, these techniques often fail to fully exploit the directed multi-graph structure and temporal dynamics of blockchain transaction graphs. This oversight, coupled with the neglect of network tail nodes, impairs the accuracy of phishing detection and distorts graph integrity. This study introduces TDM-GAT, a graph neural network that integrates directionality, edge attributes, and temporal information through transaction flow serialization and functional time encoding. This approach significantly enhances the precision of phishing account identification in blockchain networks. Additionally, a PageRank-based biased sampling strategy is implemented to address the long-tailed distribution of graph nodes, ensuring balanced node participation during learning. Evaluations on three distinct datasets show that TDM-GAT outperforms with an AUC exceeding 95% and an accuracy close to 90%, demonstrating a clear advantage in phishing account detection.

Graph Neural Networks (GNNs) are commonly used for node classification tasks in real-world scenarios. However, research has found that GNN-based classification can significantly drop in performance when the graph structure is perturbed. Current methods, such as adjusting the graph structure to reduce dependency or strengthen protection against perturbations, still have not completely solved this problem. A recent approach using Bayesian inference was introduced to handle perturbations, but this method often faces high uncertainty, which can negatively affect classification results. In this paper, we introduce a new model called WalkBayes, which combines Bayesian inference with random-walk-based label correction to improve the robustness of GNNs in handling complex perturbations. WalkBayes uses random walks to refine label correction, making better use of the graph structure to increase label propagation accuracy and reduce uncertainty. Our experiments on four graph datasets show that WalkBayes performs better than other models in GNN node classification tasks under both random and sparse perturbations to the graph structure.

Tangible interfaces in mixed reality (MR) environments allow for intuitive data interactions. Tangible cubes, with their rich interaction affordances, high maneuverability, and stable structure, are particularly well-suited for exploring multi-dimensional data types. However, the design potential of these cubes is underexplored. This study introduces a design space for tangible cubes in MR, focusing on interaction space, visualization space, sizes, and multiplicity. Using spatio-temporal data, we explored the interaction affordances of these cubes in a workshop (N=24). We identified unique interactions like rotating, tapping, and stacking, which are linked to augmented reality (AR) visualization commands. Integrating user-identified interactions, we created a design space for tangible-cube interactions and visualization. A prototype visualizing global health spending with small cubes was developed and evaluated, supporting both individual and combined cube manipulation. This research enhances our grasp of tangible interaction in MR, offering insights for future design and application in diverse data contexts.

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manip- ulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.