Neo4j Announces First Graph Machine Learning for the Enterprise
Graph-Native Machine Learning, Until Now the Domain of Big Tech, is Available with Neo4j for Graph Data Science 1.4
Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks.
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Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion.
Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises. The ability to learn generalized, predictive features from data is significant because organizations don’t always know how to represent connected data for use in machine learning models. The latest Neo4j version includes graph embedding algorithms that learn the structure of a user’s graph, rather than relying on predetermined formulas to calculate specific features like centrality scores.
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Alicia Frame, Neo4j’s Lead Product Manager and Data Scientist, shared what Neo4j for Graph Data Science version 1.4 means for data scientists and analytics teams.
“We are thrilled to bring cutting-edge graph embedding techniques into easy-to-use enterprise software,” Dr. Frame said. “The latest version of Neo4j for Graph Data Science democratizes state-of-the-science techniques and makes it possible for anyone to use graph machine learning. This is a game changer in terms of what can be achieved with predictive analysis.”
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