Event Graph

Event graphs represent events and their relationships as nodes and edges in a network, aiming to capture complex contextual information beyond individual events. Current research focuses on leveraging graph neural networks (GNNs) and large language models (LLMs) to generate, analyze, and utilize these graphs for tasks such as video question answering, media bias detection, and conspiracy theory identification. This approach improves upon previous methods by explicitly modeling event interdependencies, leading to more accurate and nuanced understanding of complex scenarios in various domains, including news analysis, social media monitoring, and acoustic scene classification. The resulting advancements have significant implications for information extraction, knowledge representation, and various downstream applications requiring contextual reasoning.