Communications of the ACM
Training Agents to Walk with Purpose
Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a new classification algorithm for relational data.
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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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