Graph Hyperdimensional Coding

graph_hdc implements hyperdimensional computing for graph structures. Graphs are encoded using a message passing network that produces high dimensional hypervectors which can be decoded again to recover structural information. The package comes with utilities for working with PyTorch Geometric Data objects, specialised encoders for node attributes and a small experiment suite based on PyComex.

Installation from source

git clone https://github.com/the16thpythonist/graph_hdc
cd graph_hdc
python3 -m pip install .

After installation the command line interface exposes the package version and the available experiments:

python3 -m graph_hdc.cli --version
python3 -m graph_hdc.cli exp list

Quickstart

The HyperNet encoder maps graphs to hypervectors. The example below encodes and decodes a small colour graph.

from graph_hdc.special.colors import generate_random_color_nx
from graph_hdc.special.colors import graph_dict_from_color_nx, make_color_node_encoder_map
from graph_hdc.models import HyperNet
from graph_hdc.graph import data_list_from_graph_dicts

# create a toy graph and convert it to the internal dict format
g = generate_random_color_nx(num_nodes=4, num_edges=5)
graph = graph_dict_from_color_nx(g)

# build the encoder and obtain the embedding
dim = 1000
net = HyperNet(hidden_dim=dim, depth=2,
               node_encoder_map=make_color_node_encoder_map(dim))
result = net.forward_graphs([graph])[0]
embedding = result['graph_embedding']
print(embedding.shape)

# decode which node types were present
constraints = net.decode_order_zero(embedding)
print(constraints)

Computational experiments

Experiments implemented with PyComex can be listed and executed via the CLI:

python3 -m graph_hdc.cli exp list
python3 -m graph_hdc.cli exp run predict_molecules__hdc

The experiments folder contains setups for comparing HDC encodings with traditional molecular fingerprints and for first steps in graph generation.

Credits

The project depends heavily on PyComex for experiment management and torch_geometric for graph operations.