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.
The code is maintained by the AI4Science team at Microsoft Research. We are hiring.
Currently this package is not under active developement, but it does continue to be used as a backend for our generative model of molecules.
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 3.7, 3.8 and 3.9.
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://data.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 --max-epochs 10 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.
The core functionality of the library is implemented as TensorFlow 2 (Keras) layers, enabling easy integration into other code.
tf2_gnn.layers.GNNThis 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.NodesToGraphRepresentationThis 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.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:
"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),"Property" key having a a single floating point value.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 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.
Marc Brockschmidt. GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation. (https://arxiv.org/abs/1906.12192)
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)
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/)
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)
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)
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)
Marinka Zitnik and Jure Leskovec. Predicting Multicellular Function Through Multi-layer Tissue Networks. Bioinformatics, 33, 2017. (https://arxiv.org/abs/1707.04638)
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