Signed Network

Signed networks, representing relationships with positive and negative edges, are increasingly studied to model complex systems where interactions are not uniformly positive. Current research focuses on developing effective graph representation learning methods, including graph convolutional networks (GCNs) and random walk algorithms adapted for signed graphs, to capture both the structure and the sign information for tasks like node classification, link prediction, and community detection. These advancements improve the accuracy and efficiency of analyzing signed networks, with applications ranging from social network analysis to understanding complex biological interactions. Furthermore, research is addressing challenges like imbalanced datasets and the limitations of existing theories, such as balance theory, in real-world scenarios.