Innovative GNNs for Complex Structures

Advanced mathematical frameworks for hyperbolic and spherical geometry in graph neural networks.

Innovative Research in Geometry-Aware GNNs

We specialize in advanced geometric frameworks for graph neural networks, focusing on hyperbolic and spherical models to enhance performance in complex data structures and relationships.

A geometric shape composed of interconnected triangular panels forms a complex three-dimensional polyhedron. Each vertex is adorned with a sphere, creating a pattern of connected nodes. The structure appears metallic and reflective against a black background.

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Trusted by Researchers

Proven Results

Advanced Geometry Models

Innovative solutions for non-Euclidean graph neural networks using hyperbolic and spherical geometries.

Hyperbolic GNN Model

Adaptive curvature learning for hierarchical structures in hyperbolic space to enhance performance.

A large geodesic sphere with a geometric, patterned surface, set against a clear blue sky.

Spherical GNN Model

Modeling cyclic dependencies through spherical space closure for improved interaction analysis.

A large geodesic sphere with a geometric pattern against a clear blue sky. Some green tree branches are visible in the lower right corner.

Geometric GNNs

Innovative models for non-Euclidean graph neural networks development.

A geometric wireframe structure that appears spherical, composed of interconnected lines and nodes, set against a plain white background.

Hyperbolic GNN

Adaptive curvature learning model designed for hierarchical structures in hyperbolic space.

A geometric structure composed of multicolored spheres connected by lines, forming a three-dimensional lattice against a dark background.

Spherical GNN

Modeling cyclic dependencies through spherical space closure for complex interactions.