Graph (Abstract Data Type)

In computing and artificial intelligence, a graph is an abstract data type that encapsulates the concept of a set of nodes (also called vertices) and the connections between them, known as edges. Graphs can be directed (where edges have a direction) or undirected (where edges have no direction), and can include weights (values associated with edges) to represent the cost or distance of the connection.

This structure is fundamental in computer science for modeling and analyzing relationships and networks in various domains, such as social networks, transportation networks, dependency graphs, and more. Graphs are central to numerous algorithms, including those for searching (like breadth-first search and depth-first search), shortest path finding (like Dijkstra's algorithm), and network flow problems.

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In AI and machine learning, graphs are extensively used in areas like natural language processing for parsing and understanding sentence structures, in recommendation systems to model the relationships between users and products, and in graph neural networks (GNNs) to process data that is inherently graph-structured, such as molecular structures or social networks.

For instance, in a knowledge graph, entities (like people, places, and things) are represented as nodes, and their relationships (like "is friends with" or "located in") are represented as edges, allowing the system to infer new knowledge or answer complex queries by traversing and analyzing the graph.

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