# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 1 Introduction  
In recent years, there has been an increasing demand for human action recognition (HAR) in various domains such as video surveillance, smart home systems, and human-computer interaction [1, 2]. Compared to RGB-based approaches [3, 4, 5, 6], skeleton-based methods have received extensive attention owing to their robustness against background clutter, scale variations, and illumination changes [7, 8, 9, 10, 11, 12, 13, 14, 15]. Skeleton data represent human bodies using 2D or 3D joint coordinates, which can either be directly captured by depth sensors [16] or estimated from image sequences using pose estimation algorithms [17]. This skeleton-based representation simplifies human actions into graph structures composed of joints and their connecting edges, effectively eliminating irrelevant background information and providing a high-level semantic abstraction. Early studies on skeleton-based HAR primarily employed Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Long Short-Term Memory (LSTM) networks were widely utilized for modeling temporal dynamics in sequential data [8, 18, 19]. Meanwhile, CNN-based methodstransformed skeleton sequences into pseudo-images and extracted spatial-temporal features using 2D CNNs, achieving improved performance [7; 10; 11; 20]. However, both RNN- and CNN-based approaches have limitations in fully leveraging the inherent topological structure of skeleton data, as skeleton data intrinsically represent graph structures rather than simple sequential vectors or 2D grids. Although RNNs and CNNs provided effective solutions for skeleton-based action recognition, they still face challenges in effectively modeling complex spatiotemporal characteristics. Given that skeleton data naturally fits a graph structure, GCNs have rapidly gained popularity in the HAR domain [21]. GCNs effectively capture joint movements and interactions among joints through convolution operations on graph-structured data, thus accurately modeling the posture variations and dynamic evolution in human actions. To overcome the limitations associated with predefined weighting mechanisms of standard GCNs and to enhance the flexibility of weight learning, Graph Attention Networks (GATs) were subsequently introduced for skeleton-based action recognition [22; 23; 24]. GATs dynamically learn node importance via attention mechanisms, thus efficiently aggregating neighboring node information. However, due to the constraints of fixed-topology graphs, GAT-based methods only utilize information from physically connected joints, making it challenging to fully explore and extract latent features between non-adjacent nodes, such as those involved in actions like clapping. To address these issues, Transformer-based approaches have recently emerged and been increasingly adopted in the HAR field [15; 25; 26; 27]. Unlike methods relying on local convolutional operations, Transformers directly model the relationships between nodes through attention mechanisms, calculating attention weights among all nodes [28]. This global-awareness capability enables Transformers to flexibly model long-range dependencies, overcoming the limited receptive field inherent in traditional GCNs. With multi-head self-attention mechanisms, Transformers effectively capture complex, multi-level dependencies between joints, including cooperative movements among non-adjacent joints as seen in actions like clapping or jumping. Nonetheless, due to the absence of inductive biases, Transformers typically require large amounts of training data to learn relational biases, and they can also introduce noise through global dependency computations, leading to computational inefficiencies [26]. To address the limitations of existing GCN- and Transformer-based methods, this paper proposes a DGAN that effectively integrates local neighborhood information with long-range dependencies.