![]() More crucially, thanks to the prevalence of graph-structured data, the availability of billions of IoT network devices, the security knowledge graph containing attack and threat semantics, the development of sizable social networking platforms, extensive scientific interoperability, even COVID-19 modeling of protein molecular structures, the amount and quality of graph-structured data accessible to researchers have significantly increased in recent years. Based on this basis, we can examine, comprehend, and learn from complex systems in the actual world. In addition to the fact that graphs come with a complete set of mathematical underpinnings, graph theory also offers a beautiful theoretical framework. It has developed into one of the areas of artificial intelligence study that is expanding the quickest from a minority subject and a small group of researchers. Over the last several years, the discipline of graph deep learning has advanced remarkably quickly. At the same time, it has a very positive impact on network security and artificial intelligence security. Following a summary of the research, we discuss problems and difficulties that must be addressed for developing future graph signal processing algorithms and graph deep learning models, such as graph embeddings’ interpretability and adversarial resilience. The attention model taking clustering into account has successfully equaled or reached the state-of-the-art performance of several well-established node classification benchmarks and does not depend on previous knowledge of the complete network structure, according to experiments. The performance of the proposed model is then assessed for transduction and induction tasks that include downstream node categorization. Second, include local neighborhood data encoded to the attention mechanism to define node solidarity and enhance node capture and interactions. The traditional approach to solving the graph representation learning issue is surveyed from machine learning and deep learning perspectives. An important issue is using neighborhood knowledge to deduce the symmetric network’s topology or graph. However, It is often undesirable to derive node representations by walking through the complete topology of a system or network (graph) when it has a very big or complicated structure. Many successful cases of graph-based models and algorithms deal with non-Euclidean structured data. Meaningful representation of large-scale non-Euclidean structured data, especially in complex domains like network security and IoT system, is one of the critical problems of contemporary machine learning and deep learning.
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