Historically, data scientists have structured their data points in a record-oriented or tabular format. This approach does not address the connections and relationships between data points that can provide valuable insight.
Graph data science offers a fresh way to visualize and explore connections between your data. It also provides a meaningful new way to explain your findings to others.
Read this guide to discover:
- How to identify a graphy problem, by asking what’s important, what’s unusual, and what’s next.
- Common uses of graph data science, from anomaly and fraud detection to supply chain optimization.
- The unique power of graph algorithms that can analyze relationships across the connected data.
- How to get smarter predictive analytics with graph data science by adding valuable context.
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