Event Relational Graph

Event relational graphs represent complex scenarios by modeling events as nodes and their relationships as edges, enabling sophisticated reasoning about sequences of events. Current research focuses on applying this framework to diverse tasks, including video question answering, acoustic scene classification, and document-level event causality identification, often employing graph neural networks and transformer architectures to learn effective graph representations. This approach offers improved performance in handling complex, multi-object interactions and provides interpretability by explicitly representing the relationships between events, leading to advancements in various fields requiring temporal and causal reasoning.