Local Cluster-Aware Attention for Non-Euclidean Structure Data

Meaningful representation of large-scale non-Euclidean structured data, especially in complex domains like network security and IoT system, is one the critical problems contemporary machine learning deep learning. Many successful cases graph-based models algorithms deal with data. However, It often undesirable to derive node representations by walking through complete topology a system or (graph) when it has very big complicated structure. An important issue using neighborhood knowledge deduce symmetric network’s graph. The traditional approach solving graph surveyed from perspectives. Second, include local data encoded attention mechanism define solidarity enhance capture interactions. performance proposed model then assessed for transduction induction tasks that downstream categorization. taking clustering into account successfully equaled reached state-of-the-art several well-established classification benchmarks does not depend on previous structure, according experiments. Following summary research, we discuss difficulties must be addressed developing future signal processing models, such as embeddings’ interpretability adversarial resilience. At same time, positive impact artificial intelligence security.