Sequence Graph

Sequence graphs represent sequential data as interconnected nodes and edges, enabling the modeling of complex relationships and dependencies within ordered information. Current research focuses on applying this framework to diverse domains, including online debate analysis, language modeling, social media prediction, and multi-hop question answering, often employing graph neural networks and attention mechanisms to process the graph structure and extract meaningful information. These advancements improve the accuracy and interpretability of models in various applications by capturing both sequential and relational aspects of the data, leading to better performance in tasks like popularity prediction and question answering.