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Roland

Dataset Format

The dataset should be prepared as a tsv file (csv is also supported with only minor midification to the loader, add keyword arg sep=',' to the dd.read_csv method), with the first row as column names.

Check TODO: 's in the roland_generic laoder to adapt it to your own dataset.

The following columns are required to construct a basic dynamic graph.

Required Fields (Columns)

  • SRC_NODE and DST_NODE: unique IDs of individuals. In BSI dataset, these two columns are named as Payer and Payee
  • TIMESTAMP: a timestamp (integer), e.g., 1230681600 denotes 2008-12-30T16:00:00. In BSI dataset, this column is named as Timestamp.
  • AMOUNT: transaction amount, this column is named as AmountEUR in BSI dataset.

Optional Fields (Columns)

  • For node features, such as the country of company, add SRC_NODECompany and DST_NODECompany columns to the dataset. In BSI dataset, the company columns are PayerCompany and PayeeCompany.
  • For edge features associated with transactions, such as the currency used in this transaction, simply add Currency column to the dataset.

Example Dataset (BSI)

  • See ./GraphGym_dev/run/datasets/bsi_synthetic.tsv for an example of BSI dataset.

Run Our Examples on BSI Dataset

  1. Since the BSI dataset is confidential, we generated a synthetic version of it for demonstration purpose. The synthetic sample has exactly the same format as BSI dataset except it’s generated randomly, so you should NOT expect any algorithm to achieve any nontrivial out of sample accuracy on it. Feel free to modify the gen sample script to change the size of generated transaction graphs. Firstly, generte a systhetic BSI dataset, there should be one there already located at: GraphGym_dev/run/datasets/bsi_synthetic.tsv. You can regenerate the dataset using the following code.

    cd GraphGym_dev/run/datasets
python ./syn_bsi.py

  2. Use the predefined YAML and RUN files, see /GraphGym_dev/run/run_single_example.sh.

    Here the ordinary recurrent GNN denotes models based on homogenous graphs (i.e., graph without edge/node types), complete heterogenous RGNN contains separate networks for message types (sender_type, edge_type, receiver_type), so there are NumNodeTypes*NumEdgeTypes*NumNodeTypes internal GNNs. The partial heterogenous GNN only consists of NumNodeTypes node feature extractors and NumEdgeTypes edge feature extractors.

    cd ./GraphGym_dev/run/
# Ordinal recurrent GNN based on homogenous graph.
python main.py --cfg configs/roland/examples/gnn_recurrent_example.yaml --repeat 1
# Complete heterogenous RGNN.
python main.py --cfg configs/roland/examples/complete_hete_example.yaml --repeat 1
# Partial heterogenous RGNN.
python main.py --cfg configs/roland/examples/partial_hete_example.yaml --repeat 1

Run Models On Your Own Dataset

To deploy existing models on your own datasets, you would need to:

  1. Make a copy of the generic loader at GraphGym_dev/graphgym/contrib/loader/roland_generic.py, modify all TODO in the python file to make it compatiable with your own dataset. (see section below.)

  2. Create the corresponding YAML and RUN files:

    cd ./GraphGym_dev/run/
python main.py --cfg YOUR_CONFIG.yaml --repeat 1

Modify yaml Configuration Files

Here we provide a detailed example explaining how to modify a config yaml file. In most cases, you only need to modify a few lines to make it work on your own dataset. Here we only include fields needed to be change.

out_dir: results
device: auto  # {'cpu', 'gpu', 'auto'}
dataset:
  format: transaction_hetero_v1  # the format needs to be compatiable with the loader.
  name: bsi_synthetic.tsv  # file name of the transaction dataset.
  is_hetero: True
  dir: /lfs/hyperturing2/0/tianyudu/GraphGym_dev/run/datasets  # dataset directory.
  task: link_pred
  shuffle: True  # must set to False to if using time series data.
  task_type: classification
  transductive: True
  split: [0.8, 0.1, 0.1]
  augment_feature: []
  augment_feature_dims: [0]
  augment_feature_repr: position
  augment_label: ''
  augment_label_dims: 0
  transform: none
  edge_encoder: True
  edge_encoder_name: roland
  edge_encoder_bn: True
  node_encoder: False
  node_encoder_name: roland
  node_encoder_bn: True
transaction:
  keep_ratio: linear
  snapshot: True
  snapshot_freq: M  # M=monthly, W=weekly, D=daily.
  check_snapshot: False
  history: rolling
  horizon: 1
  pred_mode: at
  loss: supervised
  feature_int_dim: 16  # number of categorical edge features.
  # number of unique values for each categorical edge feature, for example, 1017 means the first categorical
  # edge feature (which is PayerBank in BSI dataset) has 1017 unique values. This needs to be modified based on
  # the dataset.
  feature_edge_int_num: [1017, 1018, 33, 33, 13, 13, 23, 23, 86, 86, 5, 5, 9, 9, 1, 1]
  feature_node_int_num: [1, 1]
  feature_amount_dim: 16
  feature_time_dim: 16
train:
  batch_size: 32
  eval_period: 5
  ckpt_period: 400
  # See ./GraphGym_dev/graphgym/contrib/train/ for avaliable for `mode`.
  mode: new_hetero  # which training module to use.
model:
  # See ./GraphGym_dev/graphgym/contrib/network/ for all options.
  type: hetero_gnn_recurrent
  loss_fun: cross_entropy
  edge_decoding: concat  # Only use node embeddings.
  graph_pooling: add
gnn:
  layers_pre_mp: 2  # number of fully-connected before GNN.
  layers_mp: 2  # number of GNN layers.
  layers_post_mp: 2  # number of fully-connected after GNN.
  dim_inner: 128  # dimension of hidden layers in GNN.
  # See ./GraphGym_dev/graphgym/contrib/layer/ for all options.
  layer_type: generaledgeheteconv_complete
  stage_type: stack
  batchnorm: True
  act: prelu
  dropout: 0.0
  agg: add
  att_heads: 4
  normalize_adj: False
optim:
  optimizer: adam
  base_lr: 0.01
  max_epoch: 100