Auditory attention detection (AAD) aims to identify the attended speaker in multi-talker environments by analyzing brain activity recorded through neural monitoring techniques. Recent AAD approaches have achieved great progress in improving detection accuracy. However, they still face challenges in capturing complex spatio-temporal dependencies and high-order nonlinear relationships across brain regions. To address these challenges, this paper proposes DHGCN, a dual hypergraph convolutional network that integrates a hypergraph modeling module, a dual-branch hypergraph learning (DHGL) module, and a feature fusion module. Specifically, the hypergraph modeling module constructs spatial and temporal hypergraphs from EEG signals, enabling the representation of high-order relationships among channels and time points. The DHGL module comprises two parallel branches: a spatial branch that learns high-order spatial dependencies across EEG channels, and a temporal branch that captures complex temporal dependencies. Each branch uses its corresponding hypergraph structure, which is established during the modeling phase. The feature fusion module then aggregates spatial and temporal representations from both branches to support robust auditory attention classification. Extensive experiments on multiple benchmark datasets demonstrate that DHGCN consistently outperforms state-of-the-art AAD models. It achieves superior classification performance while reducing the trainable parameters count by over 50% compared to the state-of-the-art models. Code is available at: https://github.com/nobody1219/DHGCN.git.

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.