SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

This repository contains the implementation of SNoRe algorithm from SNoRe paper found here:

@misc{meznar2020snore,
    title={SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations},
    author={Sebastian Me\v{z}nar and Nada Lavra\v{c} and Bla\v{z} \v{S}krlj},
    year={2020},
    eprint={2009.04535},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

An overview of the algorithm is presented in the image below:

Installing SNoRe

python setup.py install

or

pip install snore-embedding

Using SNoRe

A simple use-case is shown below. First, we import the necessary libraries and load the dataset and its labels.

from snore import SNoRe
from scipy.io import loadmat
from sklearn.utils import shuffle
from catboost import CatBoost
import pandas as pd
from sklearn.metrics import f1_score
import numpy as np

# Load adjacency matrix and labels
dataset = loadmat("../data/cora.mat")
network_adj = dataset["network"]
labels = dataset["group"]

We then create the SNoRe model and embed the network. In code, the default parameters are shown.

# Create the model
model = SNoRe(dimension=256, num_walks=1024, max_walk_length=5,
              inclusion=0.005, fixed_dimension=False, metric="cosine",
              num_bins=256)

# Embed the network
embedding = model.embed(network_adj)

Finally, we train the classifier and test on the remaining data.

# Train the classifier
nodes = shuffle([i for i in range(network_adj.shape[0])])
train_mask = nodes[:int(network_adj.shape[0]*0.8)]
test_mask = nodes[int(network_adj.shape[0]*0.8):]
classifier = CatBoost(params={'loss_function': 'MultiRMSE', 'iterations': 500})
df = pd.DataFrame.sparse.from_spmatrix(embedding)
classifier.fit(df.iloc[train_mask], labels[train_mask])

# Test prediction
predictions = classifier.predict(df.iloc[test_mask])
print("Micro score:",
      f1_score(np.argmax(labels[test_mask], axis=1),
               np.argmax(predictions, axis=1),
               average='micro'))

Further examples of evaluation and embedding explainability can be found in the example folder.