amine-chaabouni/tf2-gnn

TensorFlow 2 library implementing Graph Neural Networks

Graph Neural Networks in TF2

Implementation and example training scripts of various flavours of graph neural network in TensorFlow 2.0. Much of it is based on the code in the tf-gnn-samples repo.

Installation

You can install the tf2_gnn module from the Python Package Index using pip install tf2_gnn.

Alternatively (for example, for development), you can check out this repository, navigate to it and run pip install -e ./ to install it as a local editable package.

You will then be able to use the tf2_gnn.layers.GNN class and related utilities.

This code was tested in Python 3.6 and 3.7 with TensorFlow 2.0 and 2.1.

The code is maintained by the All Data AI group at Microsoft Research, Cambridge, UK. We are hiring.

Testing the Installation

To test if all components are set up correctly, you can run a simple experiment on the protein-protein interaction (PPI) task first described by Zitnik & Leskovec, 2017. You can download the data for this task from https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/ppi.zip and unzip it into a local directory (e.g., data/ppi). Then, you can use the convenience utility tf2_gnn_train (see --help for a description of options) to train a Relational Graph Convoluational Network model as follows:

$ tf2_gnn_train RGCN PPI data/ppi/
Setting random seed 0.
Trying to load task/model-specific default parameters from /dpuhome/files/users/mabrocks/Projects/TF2-GNN/tf2_gnn/cli_utils/default_hypers/PPI_RGCN.json ... File found.
 Dataset default parameters: {'max_nodes_per_batch': 10000, 'add_self_loop_edges': True, 'tie_fwd_bkwd_edges': False}
Loading data from data/ppi/.
 Loading PPI train data from data/ppi/.
 Loading PPI valid data from data/ppi/.
[...]
Dataset parameters: {"max_nodes_per_batch": 8000, "add_self_loop_edges": true, "tie_fwd_bkwd_edges": false}
Model parameters: {"gnn_aggregation_function": "sum", "gnn_message_activation_function": "ReLU", "gnn_hidden_dim": 320, "gnn_use_target_state_as_input": false, "gnn_normalize_by_num_incoming": true, "gnn_num_edge_MLP_hidden_layers": 0, "gnn_message_calculation_class": "RGCN", "gnn_initial_node_representation_activation": "tanh", "gnn_dense_intermediate_layer_activation": "tanh", "gnn_num_layers": 4, "gnn_dense_every_num_layers": 10000, "gnn_residual_every_num_layers": 10000, "gnn_use_inter_layer_layernorm": false, "gnn_layer_input_dropout_rate": 0.1, "gnn_global_exchange_mode": "gru", "gnn_global_exchange_every_num_layers": 10000, "gnn_global_exchange_weighting_fun": "softmax", "gnn_global_exchange_num_heads": 4, "gnn_global_exchange_dropout_rate": 0.2, "optimizer": "Adam", "learning_rate": 0.001, "learning_rate_decay": 0.98, "momentum": 0.85, "gradient_clip_value": 1.0}
Initial valid metric: Avg MicroF1: 0.368.
   (Stored model metadata to trained_model/RGCN_PPI__2020-02-25_11-10-38_best.pkl and weights to trained_model/RGCN_PPI__2020-02-25_11-10-38_best.hdf5)
== Epoch 1
 Train:  25.6870 loss | Avg MicroF1: 0.401 | 2.63 graphs/s
 Valid:  33.1668 loss | Avg MicroF1: 0.419 | 4.01 graphs/s
  (Best epoch so far, target metric decreased to -0.41886 from -0.36762.)
   (Stored model metadata to trained_model/RGCN_PPI__2020-02-25_11-10-38_best.pkl and weights to trained_model/RGCN_PPI__2020-02-25_11-10-38_best.hdf5)
[...]

After training finished, tf2_gnn_test trained_model/RGCN_PPI__2020-02-25_11-10-38_best.pkl data/ppi can be used to test the trained model.

Code Structure

Layers

The core functionality of the library is implemented as TensorFlow 2 (Keras) layers, enabling easy integration into other code.

tf2_gnn.layers.GNN

This implements a deep Graph Neural Network, stacking several layers of message passing. On construction, a dictionary of hyperparameters needs to be provided (default values can be obtained from GNN.get_default_hyperparameters()). These hyperparameters configure the exact stack of GNN layers:

• "num_layers" sets the number of GNN message passing layers (usually, a number between 2 and 16)

• "message_calculation_class" configures the message passing style. This chooses the tf2_gnn.layers.message_passing.* layer used in each step.

  We currently support the following:

  • GGNN: Gated Graph Neural Networks (Li et al., 2015).
  • RGCN: Relational Graph Convolutional Networks (Schlichtkrull et al., 2017).
  • RGAT: Relational Graph Attention Networks (Veličković et al., 2018).
  • RGIN: Relational Graph Isomorphism Networks (Xu et al., 2019).
  • GNN-Edge-MLP: Graph Neural Network with Edge MLPs - a variant of RGCN in which messages on edges are computed using full MLPs, not just a single layer applied to the source state.
  • GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation (Brockschmidt, 2019) - a new extension of RGCN with FiLM layers.

  Some of these expose additional hyperparameters; refer to their implementation for details.

• "hidden_dim" sets the size of the output of all message passing layers.

• "layer_input_dropout_rate" sets the dropout rate (during training) for the input of each message passing layer.

• "residual_every_num_layers" sets how often a residual connection is inserted between message passing layers. Concretely, a value of k means that every layer l that is a multiple of k (and only those!) will not receive the outputs of layer l-1 as input, but instead the mean of the outputs of layers l-1 and l-k.

• "use_inter_layer_layernorm" is a boolean flag indicating if LayerNorm should be used between different message passing layers.

• "dense_every_num_layers" configures how often a per-node representation dense layer is inserted between the message passing layers. Setting this to a large value (greather than "num_layers") means that no dense layers are inserted at all.

  "dense_intermediate_layer_activation" configures the activation function used after the dense layer; the default of "tanh" can help stabilise training of very deep GNNs.

• "global_exchange_every_num_layers" configures how often a graph-level exchange of information is performed. For this, a graph level representation (see tf2_gnn.layers.NodesToGraphRepresentation below) is computed and then used to update the representation of each node. The style of this update is configured by "global_exchange_mode", offering three modes:

  • "mean", which just computes the arithmetic mean of the node and graph-level representation.
  • "mlp", which computes a new representation using an MLP that gets the concatenation of node and graph level representations as input.
  • "gru", which uses a GRU cell that gets the old node representation as state and the graph representation as input.

The GNN layer takes a GNNInput named tuple as input, which encapsulates initial node features, adjacency lists, and auxiliary information. The easiest way to construct such a tuple is to use the provided dataset classes in combination with the provided model.

tf2_gnn.layers.NodesToGraphRepresentation

This implements the task of computing a graph-level representation given node-level representations (e.g., obtained by the GNN layer).

Currently, this is only implemented by the WeightedSumGraphRepresentation layer, which produces a graph representation by a multi-headed weighted sum of (transformed) node representations, configured by the following hyperparameters set in the layer constructor:

• graph_representation_size sets the size of the computed representation. By setting this to 1, this layer can be used to directly implement graph-level regression tasks.
• num_heads configures the number of parallel (independent) weighted sums that are computed, whose results are concatenated to obtain the final result. Note that this means that the graph_representation_size needs to be a multiple of the num_heads value.
• weighting_fun can take two values:
  • "sigmoid" computes a weight for each node independently by first computing a per-node score, which is then squashed through a sigmoid. This is appropriate for tasks that are related to counting occurrences of a feature in a graph, where the node weight is used to ignore certain nodes.
  • "softmax" computes weights for all graph nodes together by first computing per-node scores, and then performing a softmax over all scores. This is appropriate for tasks that require identifying important parts of the graph.
• scoring_mlp_layers, scoring_mlp_activation_fun, scoring_mlp_dropout_rate configure the MLP that computes the per-node scores.
• transformation_mlp_layers, transformation_mlp_activation_fun, transformation_mlp_dropout_rate configure the MLP that computes the transformed node representations that are summed up.

Datasets

We use a sparse representation of graphs, which requires a complex batching strategy in which the graphs making up a minibatch are joined into a single graph of many disconnected components. The extensible tf2_gnn.data.GraphDataset class implements this procedure, and can be subclassed to handle task-specific datasets and additional properties. It exposes a get_tensorflow_dataset method that can be used to obtain a tf.data.Dataset that can be used in training/evaluation loops.

We currently provide three implementations of this:

• tf2_gnn.data.PPIDataset implements reading the protein-protein interaction (PPI) data first used by Zitnik & Leskovec, 2017.
• tf2_gnn.data.QM9Dataset implements reading the quantum chemistry data first used by Ramakrishnan et al., 2014.
• tf2_gnn.data.JsonLGraphPropertyDataset implements reading a generic dataset made up of graphs with a single property, stored in JSONLines format:
  • Files "train.jsonl.gz", "valid.jsonl.gz" and "test.jsonl.gz" are expected to store the train/valid/test datasets.
  • Each of the files is gzipped text file in which each line is a valid JSON dictionary with
    • a "graph" key, which in turn points to a dictionary with keys
      • "node_features" (list of numerical initial node labels),
      • "adjacency_lists" (list of list of directed edge pairs),
    • a "Property" key having a a single floating point value.

Models

We provide some built-in models in tf2_gnn.models, which can either be directly re-used or serve as inspiration for other models:

• tf2_gnn.models.GraphRegressionTask implements a graph-level regression model, for example to make molecule-level predictions such as in the QM9 task.
• tf2_gnn.models.GraphBinaryClassificationTask implements a binary classification model.
• tf2_gnn.models.NodeMulticlassTask implements a node-level multiclass classification model, suitable to implement the PPI task.

Tasks

Tasks are a combination of datasets, models and specific hyperparameter settings. These can be registered (and then used by name) using the utilities in tf2_gnn.utils.task_utils (where a few default tasks are defined as well) and then used in tools such as tf2_gnn_train.

Authors

• Henry Jackson-Flux
• Marc Brockschmidt
• Megan Stanley
• Pashmina Cameron

References

Brockschmidt, 2019

Marc Brockschmidt. GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation. (https://arxiv.org/abs/1906.12192)

Li et al., 2015

Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. Gated Graph Sequence Neural Networks. In International Conference on Learning Representations (ICLR), 2016. (https://arxiv.org/pdf/1511.05493.pdf)

Ramakrishnan et al., 2014

Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole Von Lilienfeld. Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Scientific Data, 1, 2014. (https://www.nature.com/articles/sdata201422/)

Schlichtkrull et al., 2017

Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. Modeling Relational Data with Graph Convolutional Networks. In Extended Semantic Web Conference (ESWC), 2018. (https://arxiv.org/pdf/1703.06103.pdf)

Veličković et al. 2018

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph Attention Networks. In International Conference on Learning Representations (ICLR), 2018. (https://arxiv.org/pdf/1710.10903.pdf)

Xu et al. 2019

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How Powerful are Graph Neural Networks? In International Conference on Learning Representations (ICLR), 2019. (https://arxiv.org/pdf/1810.00826.pdf)

Zitnik & Leskovec, 2017

Marinka Zitnik and Jure Leskovec. Predicting Multicellular Function Through Multi-layer Tissue Networks. Bioinformatics, 33, 2017. (https://arxiv.org/abs/1707.04638)

Contributing

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