Code for the paper "Asymptotics of ℓ2 Regularized Network Embeddings"

Related tags

Deep Learning embed-reg
Overview

README

Code for the paper Asymptotics of L2 Regularized Network Embeddings.

Requirements

Requires Stellargraph 1.2.1, Tensorflow 2.6.0, scikit-learm 0.24.1, tqdm, along with any other packages required for the above three packages.

Code

To run node classification or link prediction experiments, run

python -m code.train_embed [[args]]

or

python -m code.train_embed_link [[args]]

from the command line respectively, where [[args]] correspond to the command line arguments for each function. Note that the scripts expect to run from the parent directory of the code folder; you will need to change the import statements in the associated python files if you move them around. The -h command line argument will display the arguments (with descriptions) of each of the two files.

train_embed.py arguments

short long default help
-h --help show this help message and exit
--dataset Cora Dataset to perform training on. Available options: Cora,CiteSeer,PubMedDiabetes
--emb-size 128 Embedding dimension. Defaults to 128.
--reg-weight 0.0 Weight to use for L2 regularization. If norm_reg is True, then reg_weight/num_of_nodes is used instead.
--norm-reg Boolean for whether to normalize the L2 regularization weight by the number of nodes in the graph. Defaults to false.
--method node2vec Algorithm to perform training on. Available options: node2vec,GraphSAGE,GCN,DGI
--verbose 1 Level of verbosity. Defaults to 1.
--epochs 5 Number of epochs through the dataset to be used for training.
--optimizer Adam Optimization algorithm to use for training.
--learning-rate 0.001 Learning rate to use for optimization.
--batch-size 64 Batch size used for training.
--train-split [0.01, 0.025, 0.05] Percentage(s) to use for the training split when using the learned embeddings for downstream classification tasks.
--train-split-num 25 Decides the number of random training/test splits to use for evaluating performance. Defaults to 50.
--output-fname None If not None, saves the hyperparameters and testing results to a .json file with filename given by the argument.
--node2vec-p 1.0 Hyperparameter governing probability of returning to source node.
--node2vec-q 1.0 Hyperparameter governing probability of moving to a node away from the source node.
--node2vec-walk-number 50 Number of walks used to generate a sample for node2vec.
--node2vec-walk-length 5 Walk length to use for node2vec.
--dgi-sampler fullbatch Specifies either a fullbatch or a minibatch sampling scheme for DGI.
--gcn-activation ['relu'] Determines the activations of each layer within a GCN. Defaults to a single layer with relu activation.
--graphSAGE-aggregator mean Specifies the aggreagtion rule used in GraphSAGE. Defaults to mean pooling.
--graphSAGE-nbhd-sizes [10, 5] Specify multiple neighbourhood sizes for sampling in GraphSAGE. Defaults to [10, 5].
--tensorboard If toggles, saves Tensorboard logs for debugging purposes.
--visualize-embeds None If specified with a directory, saves an image of a TSNE 2D projection of the learned embeddings at the specified directory.
--save-spectrum None If specifies, saves the spectrum of the learned embeddings output by the algorithm.

train_embed_link.py arguments

short long default help
-h --help show this help message and exit
--dataset Cora Dataset to perform training on. Available options: Cora,CiteSeer,PubMedDiabetes
--emb-size 128 Embedding dimension. Defaults to 128.
--reg-weight 0.0 Weight to use for L2 regularization. If norm_reg is True, then reg_weight/num_of_nodes is used instead.
--norm-reg Boolean for whether to normalize the L2 regularization weight by the number of nodes in the graph. Defaults to false.
--method node2vec Algorithm to perform training on. Available options: node2vec,GraphSAGE,GCN,DGI
--verbose 1 Level of verbosity. Defaults to 1.
--epochs 5 Number of epochs through the dataset to be used for training.
--optimizer Adam Optimization algorithm to use for training.
--learning-rate 0.001 Learning rate to use for optimization.
--batch-size 64 Batch size used for training.
--test-split 0.1 Split of edge/non-edge set to be used for testing.
--output-fname None If not None, saves the hyperparameters and testing results to a .json file with filename given by the argument.
--node2vec-p 1.0 Hyperparameter governing probability of returning to source node.
--node2vec-q 1.0 Hyperparameter governing probability of moving to a node away from the source node.
--node2vec-walk-number 50 Number of walks used to generate a sample for node2vec.
--node2vec-walk-length 5 Walk length to use for node2vec.
--gcn-activation ['relu'] Specifies layers in terms of their output activation (either relu or linear), with the number of arguments determining the length of the GCN. Defaults to a single layer with relu activation.
--graphSAGE-aggregator mean Specifies the aggreagtion rule used in GraphSAGE. Defaults to mean pooling.
--graphSAGE-nbhd-sizes [10, 5] Specify multiple neighbourhood sizes for sampling in GraphSAGE. Defaults to [25, 10].
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