Network Representation

Network representation learning aims to encode complex network structures into lower-dimensional vector spaces, facilitating efficient analysis and downstream tasks like node classification and link prediction. Current research emphasizes developing novel algorithms, including graph neural networks, variational autoencoders, and tensor decomposition methods, to learn more informative and disentangled representations, often incorporating temporal dynamics and textual edge information. These advancements improve the interpretability and performance of network analysis, impacting fields ranging from social network analysis and intrusion detection to neuroscience and recommendation systems.