Explaining the decision-making process of Graph Neural Networks (GNNs) is essential for improving their transparency and reliability. However, real-world graphs are often heterogeneous and subject to structural noise, posing severe challenges to the robustness of existing explanation methods. To address these issues, we propose RoHeX, a Robust Heterogeneous GNN Explainer that enhances explanation quality under noisy conditions. RoHeX begins with a theoretical analysis revealing how different heterogeneous GNN architectures amplify structural perturbations through message passing. Building on this insight, we design a denoising variational inference framework that filters noisy structures and learns robust latent graph representations. Furthermore, we incorporate relation-aware heterogeneous semantics into the explanation generation process, formulating explanation as an optimization problem under the graph information bottleneck principle. This formulation enables RoHeX to balance fidelity and compactness, producing explanations that are both semantically meaningful and structurally stable. Comprehensive experiments on multiple real-world heterogeneous graphs demonstrate that RoHeX consistently surpasses state-of-the-art baselines in explanation fidelity, robustness to structural perturbations, and explainability.

Image recognition plays a critical role in urban security, traffic management, and environmental monitoring, yet achieving high accuracy in obstructed scenes remains a challenge. To address this, we propose a Graph Convolutional Visual Prior-Transform Network (GCVPN), which significantly improves recognition accuracy and efficiency in complex environments. GCVPN introduces an image prior slicing and topology transformer to convert image data into graph-structured slice features, integrating domain overlap sampling and planar mapping to handle symmetry and enable precise, rapid anomaly detection. By combining a traditional VGG backbone with graph convolutional layers, GCVPN jointly captures topological relationships and feature semantics, while maintaining real-time efficiency with continuous recognition at 30 video frames per second. Extensive experiments demonstrate its effectiveness in photovoltaic panel anomaly detection and face occlusion recognition, highlighting strong potential for applications in intelligent surveillance and autonomous driving.

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in handling graph data. Typically, GNNs recursively aggregate node information, including node features and local topological information, through a message-passing scheme. However, most existing GNNs are highly sensitive to neighborhood aggregation, and irrelevant information in the graph topology can lead to inefficient or even invalid node embeddings. To overcome these challenges, we propose a novel Space Gravity-based Graph Neural Network (Gravity-GNN) guided by Deep Reinforcement Learning (DRL). In particular, we introduce a novel similarity measure called ''node gravity'', inspired by the gravitational force between particles in space, to compare nodes within graph data. Furthermore, we employ DRL technology to learn and select the most suitable number of adjacent nodes for each node. Our experimental results on various real-world datasets demonstrate that Gravity-GNN outperforms state-of-the-art methods regarding node classification accuracy, while exhibiting greater robustness against disturbances.

Anomaly detection in dynamic networks aims to find network elements (e.g., nodes, edges, subgraphs, change points) with significantly different behaviors from the vast majority, it can also devote to community detection and evolution and prediction tasks. Most existing methods focus on one specific task, that is, only detect anomalies of one type of element isolated, so they lose the ability to model the correlation and driving mechanism between different abnormal behavior. Considering that the anomaly detection of one type of element is helpful to other types of elements, i.e., the temporal evolution hidden the dynamic networks are driven by indivisible behavior patterns. So in this paper, we propose a unified Generation model to analyze the dynamic network for Exploring the Abnormal Behaviors of different Scales (GEABS). It can model the relation and catch different levels (node, community and network) of anomaly with a joint statistical network model and detect the community structure and its evolution. Specifically, we denote the parameters of node popularity, community membership to generate the dynamic network with stochastic block model (SBM), we also describe the varying of node and community by dynamic process. With a well-designed generative mechanism, it can detect the change point on network level, temporal evolution on community level and abnormal behavior on node level synchronously, besides, it also detects the community structure effectively. We also propose an effective optimization algorithm with variational inference. Experimental results show that the GEABS achieves better performance on abnormal behavior and community structure compared with baselines.

The dependencies across different layers are an important property in multiplex networks and a few methods have been proposed to learn the dependencies in various ways. When capturing the dependencies across different layers, some of them assumed the structure among layers following consistent connectivity to force two nodes with a link in one layer tend to have links in other layers, some introduced a common vector to model the shared information across all layers. However, the correlations among layers in multiplex networks are diverse, which go beyond the connectivity consistency. In this paper, we propose a novel Modeling Correlations for Multiplex network Embedding (MCME) framework to learn the robust node representations for each layer. It can deal with complex correlations with a common structure, layer similarity and node heterogeneity through a unified framework in multiplex networks. To evaluate our proposed model, we conduct extensive experiments on several real-world datasets and the results demonstrate that our proposed model consistently outperforms state-of-the-art methods.

The fast growth of cloud computing has attracted more companies to migrate their in-house IT applications into cloud and it also occurs in the medical field. A mobile healthcare system with cloud offloading is considered in this paper and it can be divided into two stages: sensor network and cloud offloading. In the first stage, information collected by body sensors should be transmitted to a remote mobile device. In order to save energy, an energy-efficient transmission scheme called cooperative multi-input multi-output (MIMO) is constructed for the data transfer when allowing individual sensor nodes to cooperate with each other. In the second stage, two offloading schemes called self-reliant multi-cloud offloading system and multi-cloud offloading system are proposed and further analyzed based on serve topology and optimal graph partition. The former provides stability but with high communication cost, while the latter reduces communication cost but is less stable. Both schemes can be applied to other scenarios in which we would like to perform offloading on multiple servers.