Large Network

Research on large networks focuses on developing efficient algorithms and models to analyze and process their complex structures and dynamics, addressing computational limitations and data heterogeneity. Current efforts concentrate on distributed training frameworks for graph embeddings and federated learning, employing techniques like Leiden-Fusion partitioning, proximity-based self-organization, and graph neural networks for tasks such as node classification and centrality approximation. These advancements are crucial for handling the scale and complexity of real-world networks in diverse applications, including social networks, infrastructure monitoring, and scientific simulations, improving both efficiency and accuracy of analysis.