networks e,bedding based on machine learning algorithms

Abstract

Network embedding has made significant advancements in the field of deep learning, profoundly influencing various critical aspects of modern life. It is utilized in social networks for tasks such as community detection, recommendation systems, and fake account detection. In biological networks, network embedding facilitates protein-protein interaction analysis, the generation of new drug compounds, and protein structure analysis. Additionally, it plays a crucial role in natural language processing, such as semantic and knowledge graphs, text classification, and question-answering systems. Network embedding also finds applications in traffic prediction, route optimization, finance, and fraud detection. The use of network embedding emphasizes its significance in solving various problems across multiple domains. Graphs differ from structured data as they represent non-Euclidean data, making them more complex and challenging to handle. This complexity is particularly evident in large graphs, unordered data, and heterogeneous structures, where issues such as over-smoothing and overfitting can arise, especially when training deeper layers. Furthermore, the dynamic nature of time-evolving data poses additional challenges. A critical issue is how to effectively represent network data. By finding the right representation, advanced analytic tasks such as classification, link prediction, pattern discovery, and analysis can be conducted efficiently in both time and space. In this thesis, we present two significant advancements that play impactful roles in the field of network embedding, achieving high performance in terms of time and space. These are GSeg, a novel graph segmentation method, and Deep Graph UNets. These works address the main challenges of representation learning on graphs concerning explainability, scalability, and expressiveness. In the first part, we provide a background on the fundamentals of graph theory and machine learning that aid in understanding these works. We discuss traditional methods, including matrix factorization and random walks, as well as recent advancements in deep learning. Additionally, we highlight the failures and challenges encountered in these approaches. Finally, we present GSeg and Deep Graph UNets, detailing their hierarchical structures, results, and how they overcome previous challenges, along with a demonstration