Graphs and Relational Reasoning
Understanding connections that drive intelligence.
Graph technologies and relational reasoning empower AI systems to model, analyze, and infer from complex relationships between entities, not just isolated data points.
At Dot Square Lab, we apply advanced graph-based techniques to uncover hidden patterns, optimize systems, and enhance decision-making across dynamic, interconnected domains.
By modeling relationships explicitly, we enable smarter, more context-aware AI solutions that adapt to real-world complexity.
What we build with
Leverage deep learning architectures specifically designed to operate over graphs for powerful relational insights.
Create structured representations of knowledge from unstructured and semi-structured data.
Predict missing or future connections between entities to power recommendations, fraud detection, and social graph analysis.
Uncover natural groupings, communities, and structures within complex datasets.
Enable AI systems to perform logical inference, pathfinding, and optimization across interconnected nodes.
Transform graph structures into vector representations for downstream machine learning applications.
Applications
Detect suspicious patterns in transaction networks, communications, and supply chains.
Power product, content, or connection recommendations by analyzing user-item graphs and social graphs.
Map and optimize complex supply networks to improve efficiency and resilience.
Model relationships among genes, diseases, treatments, and patients for better diagnostics and drug discovery.
Build dynamic, interconnected knowledge bases that improve retrieval, navigation, and organizational memory.
Monitor, optimize, and secure large-scale communications and IT infrastructure.
