Graph Sequence

Graph sequence analysis focuses on understanding and modeling data represented as a series of interconnected graphs, aiming to extract meaningful patterns and predictions from evolving network structures. Current research emphasizes developing efficient algorithms for tasks like dense subgraph discovery in temporal networks and integrating graph representations with other models, such as sequence-to-sequence architectures for speech synthesis or language models for keyphrase extraction. These advancements are improving performance in diverse applications, including financial trend prediction, text analysis, and speech processing, by leveraging the rich relational information inherent in graph sequences.