Graph Machine Learning
Graph analytics and graph machine learning are both approaches to analyzing and deriving insights from graph-structured data, but they differ significantly in their methodologies, goals, and applications.
In essence, Graph Analytics is focused on analyzing and understanding the existing structure of a graph using predefined algorithms. Graph Machine Learning, on the other end, goes a step further by using machine learning techniques to learn from graph data, enabling predictions and deeper insights that can be generalized beyond the immediate structure of the graph.
Our Services
Graph Data Analysis and Modeling
- Data Preparation & Transformation (ETL): we assist in transforming raw data into graph structures, ensuring that your data is optimally prepared for analysis.
- Graph Database Design: whether you’re new to graph databases or need to optimize an existing setup, we help you design and implement efficient graph databases (both RDF and property graphs) tailored to your needs.
- Network Analysis: gain insights into your network’s structure, identify key nodes and edges, and understand the dynamics within your data.
- Development: graph data science based on PyTorch Geometric (PyG) and Deep Graph Learning (DGL).
Graph-Based Machine Learning
- Graph Neural Networks (GNNs): We specialize in implementing and optimizing GNNs to enable learning from graph-structured data, helping you uncover patterns and make predictions with high accuracy. We work primarily with PyTorch Geometric (ala PyG) but also have experience with DGL, StellarGraph and more.
- Node & Link Prediction: Whether you’re predicting future interactions, identifying missing links, or detecting fraud, our expertise in node and link prediction will provide you with actionable insights.
- Recommendation Systems: Leverage graph-based approaches to create personalized recommendation systems that outperform traditional methods in accuracy and scalability.
End-to-end Solutions
Graphs are central to our business, driving the solutions we offer. We provide end-to-end development services and expert guidance across the entire graph spectrum, ensuring that our clients can leverage this powerful technology to its fullest potential.
As graph machine learning often involves handling large datasets, we specialize in building robust big-data infrastructures and data pipelines. Our services extend to creating middleware and dashboarding solutions that seamlessly integrate with complex data environments, enabling efficient analysis and decision-making.
