Graph-based methods are used in information retrieval (IR) to model the relationships between documents, terms, or users. By representing the information as a graph, where nodes represent entities and edges represent relationships, these methods can efficiently capture the structure and dependencies within the data. For example, in web search, link analysis algorithms like PageRank treat the web as a graph, where web pages are nodes and hyperlinks are edges, to rank the relevance of pages.
Graph-based IR methods can also model semantic relationships between terms using techniques like knowledge graphs, which enable the system to go beyond exact keyword matches and understand the context. This is useful for improving search quality, recommendation systems, and personalized content.
Overall, graph-based methods provide a powerful tool for handling complex and interrelated data in IR, making them ideal for tasks like query expansion, document retrieval, and ranking.