Learning Network Representations

Learning network representations is a technique that maps complex network structures into low-dimensional vector spaces, aiming to preserve the topological structure and node attribute information of the network. Its core objective is to enhance the performance of network data in machine learning tasks through effective representation learning, such as node classification, link prediction, and community detection. This technology has significant application value in fields like network science, social computing, and recommendation systems, enabling the effective mining and utilization of latent patterns and relationships in network data.