Σ-Graphion™
hyperdimensional graph neural network
Unlock the Power of AI That Understands Complex Relationships
inference speed 1.5× – 10×
accuracy gain 25% – 40%
billion‑scale graphs (OGB, Freebase)
Dataset:
Graph complexity:
70%
GraphML / OGB validated
inference speed (ms per node traversal)
OpenGraphBench · GNN/GAT
traditional GNN
Σ-Graphion
predictive accuracy (%)
traditional
Σ-Graphion
Σ-Graphion vs traditional – multi‑dimension
scalability – billion‑node graphs
benchmark details – OpenGraphBench
| hardware / model |
trad. speed (ms) |
Σ‑Graphion (ms) |
speedup |
trad. acc. (%) |
Σ‑Graphion (%) |
gain |
Benchmarked on OGB, YelpGraph, Freebase, OpenGraphBench
GPU: NVIDIA A100, CPU: Intel Xeon, TPU: v4
⚡ Σ-Graphion advantage
- 1.5× – 10× inference speedup per node traversal
- 25% – 40% higher accuracy (node/link/anomaly)
- 10× computational efficiency on billion‑scale graphs
- +50% explainability via graph reasoning
traditional AI baseline
- optimized for tabular data only
- struggles with unseen relationships
- black‑box models, low interpretability
- fails on billion‑scale interconnected data
Graph Neural Networks (GNNs)
Training time ↓30‑50% vs transformers. High‑order reasoning.
Topological Data Analysis
Anomaly detection +30‑50%, false positives ↓40%.
Hypergraph Neural Networks
Multi‑way interactions: 2×‑3× efficiency, memory ↓30%.
optimized deployment – geometric deep learning
PyTorch Geometric (PyG)
Deep Graph Library (DGL)
GraphBLAS
GPU/TPU/FPGA
✔ 5×‑10× efficiency vs MLP, 40% complexity reduction via tensor decomposition, dynamic knowledge graph updates.
quantum‑enhanced graph neural networks
Targeting 15× faster inference through quantum‑inspired learning.
self‑supervised / multi‑agent AI
Reduce training data by 50%, federated graph learning.