Graph Extraction

Graph extraction focuses on automatically converting diverse data sources—including images (e.g., circuit diagrams, floor plans), text, and simulation outputs—into graph representations, facilitating analysis and knowledge discovery. Current research emphasizes developing robust and efficient algorithms, often employing graph convolutional networks or contrastive learning methods, to handle various data types and complexities, including noisy or incomplete information. These techniques find applications across numerous fields, improving tasks such as keyphrase recommendation, protein structure analysis, autonomous vehicle perception, and crash simulation analysis by enabling more effective data modeling and knowledge extraction.