Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a classification algorithm for relational data that is more accurate and orders of magnitude more efficient than previous methods.
The new algorithm represents a more robust approach to classifying relational data by introducing machine learning techniques.
Classifying relational data involves a search agent taking an exploratory "walk" following the connections among nodes.
The algorithm is a graph-based classification model that trains the agent using a reinforcement learning method, which achieves a better classification result.
The new method "is also generally applicable to any kind of graph-structured data, such as social-network recommendation systems and classification of biomolecules, as well as cybersecurity," says NortonLifeLock researcher Han Yufei.
From KAUST Discovery
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA
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