Graph buildings are pervasive in real-world information, underpinning domains starting from molecular chemistry to data graphs, software program summary syntax bushes, and social networks. Whereas Graph Neural Networks (GNNs) have confirmed highly effective for processing such information, they usually lack the power to motive symbolically, generalize from construction, and carry out analogical considering with out in depth retraining.
This text explores a strong hybrid strategy that mixes neural networks with Vector Symbolic Architectures (VSAs) to categorise, infer, and motive over symbolic graph representations. We display a full implementation that:
- Learns to categorise graphs based mostly on symbolic encodings
- Visualizes VSA graph embeddings utilizing PCA
- Performs zero-shot classification on unseen buildings
- Executes analogical reasoning chains with symbolic arithmetic
By means of a compact and environment friendly Python implementation, this venture illustrates how neuro-symbolic computing can bridge the hole between neural studying and symbolic logic, unlocking new capabilities in…
