node_classification with GNN

Node classification is the problem of finding out the right label for a node based on its neighbors’ labels and structure similarities.

The node_classification module supports as follows:

• homogeneous and heterogeneous graphs,
• multiple-label and multi-edge-type graphs,
• any-size datasets,
• the following model architectures:
  • Graph Attention with Jumping Knowledge,
  • multiple versions of Graph attention networks (GAT),
  • GraphSAGE,
• early stopping,
• calculation of various metrics,
• predictions for specified nodes,
• model saving and loading,
• recommendation system use cases.

If you don’t have a vector at the beginning, which will be input for the GNN, then you can call one of the centrality algorithms (PageRank, betwenness centrality, etc.) and save all values into one vector, which will be input for the ML algorithm. That vector usually consists of numbers that describe the dataset in a way that is useful for a particular use case. If that vector is well-defined for the use case, you can expect better results, but if the vector is irrelevant to the use case, you’ll probably get results that are not that useful.

Procedures

This query module contains all necessary functions you need to train GNN model on Memgraph.

The basic node classification workflow is as follows:

1. Load data to Memgraph.
2. Set parameters by calling the set_model_parameters() procedure.
3. Start the training by calling the train() procedure.
4. Inspect the training results (optional) by calling get_training_data() procedures.
5. Save or load results using the save_model() and load_model() procedures.
6. Predict a node class by calling the predict() procedure.

set_model_parameters()

The set_model_parameters() procedure initializes all global variables. Before calling the procedure, be sure that node_features property on nodes are in place.

Input:

• subgraph: Graph (OPTIONAL) ➡ A specific subgraph, which is an object of type Graph returned by the project() function, on which the algorithm is run. If subgraph is not specified, the algorithm is computed on the entire graph by default.

• params: (mgp.Map, optional): User defined parameters from query module. Defaults to .

Output:

• mgp.Record(hidden_features_size=list, layer_type=str, aggregator=str, learning_rate=float, weight_decay=float, split_ratio=float, metrics=mgp.Any, node_id_property=str, num_epochs=int, console_log_freq=int, checkpoint_freq=int, device_type=str, path_to_model=str) ➡ Map of parameters set for training

Usage:

When calling the procedure, send only those parameters that need changing from their default values.

  CALL node_classification.set_model_parameters(
    {layer_type: "GATJK", learning_rate: 0.001, hidden_features_size: [16,16], class_name: "fraud", features_name: "embedding"}
  ) YIELD aggregator, metrics
  RETURN aggregator, metrics;

train()

The train() procedure performs model training. Firstly it declares data, model, optimizer, and criterion. Afterward, it performs training.

Input:

• subgraph: Graph (OPTIONAL) ➡ A specific subgraph, which is an object of type Graph returned by the project() function, on which the algorithm is run. If subgraph is not specified, the algorithm is computed on the entire graph by default.

• num_epochs (int, optional) ➡ Number of epochs (default:100).

Output:

• epoch: int ➡ Epoch number.
• loss: float➡ Loss of model on training data.
• val_loss: float➡ Loss of model on validation data.
• train_log: list➡ List of metrics on training data.
• val_log: list➡ List of metrics on validation data.

Usage:

To train the model, use the following query:

  CALL node_classification.train() 
  YIELD epoch, loss, val_loss 
  RETURN epoch, loss, val_loss;

get_training_data()

Use the get_training_data() procedure to get logged data from training.

Output:

• epoch: int ➡ Epoch number for current record’s logged data.
• loss: float➡ Loss in epoch.
• train_log: mgp.Any ➡ Training parameters for epoch.
• val_log: mgp.Any➡ Validation parameters for epoch.

Usage:

To get logged data, use the following query:

  CALL node_classification.get_training_data() 
  YIELD epoch, loss
  RETURN epoch, loss;

save_model()

The save_model() procedure saves the model to a specified directory. If there are already 5 models in the directory, the oldest model is deleted.

Output:

• path (str)➡ Path to the stored model.
• status (str)➡ Status of the stored model.

Usage:

To save the model, use the following query:

  CALL node_classification.save_model() 
  YIELD path, status
  RETURN path, status;

load_model()

This load_model() procedure loads the model from the specified folder.

Input:

• subgraph: Graph (OPTIONAL) ➡ A specific subgraph, which is an object of type Graph returned by the project() function, on which the algorithm is run. If subgraph is not specified, the algorithm is computed on the entire graph by default.

• num (int, optional) ➡ Ordinal number of model to load from the default path on the disc (default: 0, i.e., newest model).

Output:

• path: str ➡ Path of loaded model.

Usage:

To load a model, use the following query:

  CALL node_classification.load_model()
  YIELD path
  RETURN path;

predict()

This procedure predicts metrics on one node. It is suggested to load the test data (data without labels) as well. Test data won’t be a part of the training or validation process.

Input:

• subgraph: Graph (OPTIONAL) ➡ A specific subgraph, which is an object of type Graph returned by the project() function, on which the algorithm is run. If subgraph is not specified, the algorithm is computed on the entire graph by default.

• vertex: mgp.Vertex➡ Prediction node.

Output:

• predicted_class: int➡ Predicted class for specified node.

Usage:

To predict node classification, use the following query:

MATCH (n {id: 1}) CALL node_classification.predict(n) 
YIELD predicted_class
RETURN predicted_class;

reset()

This function resets all variables to default values.

Output:

• status (str) ➡ Status of reset function.

Usage:

To reset the values to their default values, use the following query:

  CALL node_classification.reset()
  YIELD status
  RETURN status;

Example

CREATE (v1:PAPER {id: 10, features: [1, 2, 3], label:0});
CREATE (v2:PAPER {id: 11, features: [1.54, 0.3, 1.78], label:0});
CREATE (v3:PAPER {id: 12, features: [0.5, 1, 4.5], label:0});
CREATE (v4:PAPER {id: 13, features: [0.78, 0.234, 1.2], label:0});
CREATE (v5:PAPER {id: 14, features: [3, 4, 100], label:0});
CREATE (v6:PAPER {id: 15, features: [2.1, 2.2, 2.3], label:1});
CREATE (v7:PAPER {id: 16, features: [2.2, 2.3, 2.4], label:1});
CREATE (v8:PAPER {id: 17, features: [2.3, 2.4, 2.5], label:1});
CREATE (v9:PAPER {id: 18, features: [2.4, 2.5, 2.6], label:1});
MATCH (v1:PAPER {id:10}), (v2:PAPER {id:11}) CREATE (v1)-[e:CITES {}]->(v2);
MATCH (v2:PAPER {id:11}), (v3:PAPER {id:12}) CREATE (v2)-[e:CITES {}]->(v3);
MATCH (v3:PAPER {id:12}), (v4:PAPER {id:13}) CREATE (v3)-[e:CITES {}]->(v4);
MATCH (v4:PAPER {id:13}), (v1:PAPER {id:10}) CREATE (v4)-[e:CITES {}]->(v1);
MATCH (v4:PAPER {id:13}), (v5:PAPER {id:14}) CREATE (v4)-[e:CITES {}]->(v5);
MATCH (v5:PAPER {id:14}), (v6:PAPER {id:15}) CREATE (v5)-[e:CITES {}]->(v6);
MATCH (v6:PAPER {id:15}), (v7:PAPER {id:16}) CREATE (v6)-[e:CITES {}]->(v7);
MATCH (v7:PAPER {id:16}), (v8:PAPER {id:17}) CREATE (v7)-[e:CITES {}]->(v8);
MATCH (v8:PAPER {id:17}), (v9:PAPER {id:18}) CREATE (v8)-[e:CITES {}]->(v9);
MATCH (v9:PAPER {id:18}), (v6:PAPER {id:15}) CREATE (v9)-[e:CITES {}]->(v6);

CALL node_classification.set_model_parameters({layer_type: "GAT", learning_rate: 0.001, hidden_features_size: [2,2], class_name: "label", features_name: "features", console_log_freq:1}) 
YIELD aggregator, metrics
RETURN aggregator, metrics;

CALL node_classification.train(5)
YIELD epoch, loss RETURN *;

+----------+----------+
| epoch    | loss     |
+----------+----------+
| 1        | 0.788709 |
| 2        | 0.765075 |
| 3        | 0.776351 |
| 4        | 0.727615 |
| 5        | 0.727735 |

MATCH (v1:PAPER {id: 10})
CALL node_classification.predict(v1) 
YIELD predicted_class 
RETURN predicted_class, v1.label as correct_class;

+-----------------+-----------------+
| predicted_class | correct_class   |
+-----------------+-----------------+
| 0               | 0               |
+-----------------+-----------------+