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temporal_graph_networks

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Abstract​

The temporal_graph_networks (TGNs) are a type of graph neural network (GNN) for dynamic graphs. In recent years, GNNs have become very popular due to their ability to perform a wide variety of machine learning tasks on graphs, such as link prediction, node classification, and so on. This rise started with Graph convolutional networks (GCN) introduced by Kipf et al., followed by GraphSAGE introduced by Hamilton et al., and recently a new method that introduces the attention mechanism to graphs was presented - Graph attention networks (GAT), by VeličkoviΔ‡ et al. The last two methods offer a great possibility for inductive learning. But they haven't been specifically developed to handle different events occurring on graphs, such as node features updates, node deletion, edge deletion and so on. These events happen regularly in real-world examples such as the Twitter network, where users update their profile, delete their profile or just unfollow another user.

In their work, Rossi et al. introduce Temporal graph networks, an architecture for machine learning on streamed graphs, a rapidly-growing ML use case.

About the query module​

What we have got in this module:

  • link prediction - train your TGN to predict new links/edges and node classification - predict labels of nodes from graph structure and node/edge features
  • graph attention layer embedding calculation and graph sum layer embedding layer calculation
  • mean and last as message aggregators
  • mlp and identity(concatenation) as message functions
  • gru and rnn as memory updater
  • uniform temporal neighborhood sampler
  • memory store and raw message store

as introduced by Rossi et al..

The following means you can use TGN to predict edges or perform node classification tasks, with graph attention layer or graph sum layer, by using either mean or last as message aggregator, mlp or identity as message function, and finally gru or rnn as memory updater.

In total, this gives you 2βœ•2βœ•2βœ•2βœ•2 options, that is, 32 options to explore on your graph! πŸ˜„

If you want to explore our implementation, jump to github/memgraph/mage and find python/tgn.py. You can also jump to the download page, download Memgraph Platform and fire up TGN. We have prepared an Amazon user-item dataset on which you can explore link prediction using a Jupyter Notebook

What is not implemented in the module:

  • node update/deletion events since they occur very rarely - although we have prepared a codebase to easily integrate them.
  • edge deletion events
  • time projection embedding calculation and identity embedding calculation since the author mentions they perform very poorly on all datasets
    • although it is trivial to add a new layer

Feel free to open a GitHub issue or start a discussion on Discord if you want to speed up development.

How should you use the following module? Prepare Cypher queries, and split them into a train set and eval set. Don't forget to call the set_mode method. Every result is stored so that you can easily get it with the module. The module reports the mean average precision for every batch training or evaluation done.

Usage​

The following procedure is expected when using TGN:

  • set parameters by calling set_params() function
  • set trigger on edge create event to call update() function
  • start loading your train queries
  • when train queries are loaded, switch TGN mode to eval by calling set_eval() function
  • load eval queries
  • do a few more epochs of training and evaluation to get the best results by calling train_and_eval()

One thing is important to mention: by calling set_eval() function you change the mode of temporal graph networks to eval mode. Any new edges which arrive will not be used to train the module, but to eval.

Implementation details​

Query module​

The module is implemented using PyTorch. From the input (mgp.Edge and mgp.Vertex labels), edge features and node features are extracted. With a trigger set, the update query module procedure will parse all new edges and extract the information the TGN needs to do batch by batch processing.

On the following piece of code, you can see what is extracted from edges while the batch is filling up. When the current processing batch size reaches batch size (predefined in set()), we forward the extracted information to the TGN, which extends torch.nn.Module.

@dataclasses.dataclass
class QueryModuleTGNBatch:
current_batch_size: int
sources: np.array
destinations: np.array
timestamps: np.array
edge_idxs: np.array
node_features: Dict[int, torch.Tensor]
edge_features: Dict[int, torch.Tensor]
batch_size: int
labels: np.array

Processing one batch​

        self._process_previous_batches()

graph_data = self._get_graph_data(
np.concatenate([sources.copy(), destinations.copy()], dtype=int),
np.concatenate([timestamps, timestamps]),
)

embeddings = self.tgn_net(graph_data)

... process negative edges in a similar way

self._process_current_batch(
sources, destinations, node_features, edge_features, edge_idxs, timestamps
)

Our torch.nn.Module is organized as follows:

  • processing previous batches - as in the research paper this will include new computation of messages collected for each node with a message function, aggregation of messages for each node with a message aggregator and finally updating of each node's memory with a memory updater
  • afterward, we create a computation graph used by the graph attention layer or graph sum layer
  • the final step includes processing the current batch, creating new interaction or node events, and updating the raw message store with new events

The process repeats: as we get new edges in a batch, the batch fills, and the new edges are forwarded to the TGN and so on.

info

This MAGE module is still in its early stage. We intend to use it only for learning activities. The current state of the module is that you need to manually switch the TGN mode to eval. After the switch, incoming edges will be used for evaluation only. If you wish to make it production-ready, make sure to either open a GitHub issue or drop us a comment on Discord. Also, consider throwing us a ⭐ so we can continue to do even better work.

TraitValue
Module typemodule
ImplementationPython
Graph directiondirected
Edge weightsweighted/unweighted
Parallelismsequential

Procedures​

info

If you want to execute this algorithm on graph projections, subgraphs or portions of the graph, be sure to check out the guide on How to run a MAGE module on subgraphs.

set_params(params)​

We have defined default value for each of the parameters. If you wish to change any of them, call the method with the defined new value.

Input:​

  • params: mgp.Map ➑ a dictionary containing the following parameters:
NameTypeDefaultDescription
learning_typeStringself_supervisedself_supervised or supervised depending on if you want to predict edges or node labels
batch_sizeInteger200size of batch to process by TGN, recommended size 200
num_of_layersInteger2number of layers of graph neural network, 2 is the optimal size, GNNs perform worse with more layers in terms of time needed to train, but the gain in accuracy is not significant
layer_typeStringgraph_attngraph_attn or graph_sum layer type as defined in the original paper
memory_dimensionInteger100dimension of memory tensor of each node
time_dimensionInteger100dimension of time vector from time2vec paper
num_edge_featuresInteger50number of edge features we will use from each edge
num_node_featuresInteger50number of expected node features
message_dimensionInteger100dimension of the message, only used if you use MLP as the message function type, otherwise ignored
num_neighborsInteger15number of sampled neighbors
edge_message_function_typeStringidentitymessage function type, identity for concatenation or mlp for projection
message_aggregator_typeStringlastmessage aggregator type, mean or last
memory_updater_typeStringgrumemory updater type, gru or rnn
num_attention_headsInteger1number of attention heads used if you define graph_attn as layer type
learning_rateFloat1e-4learning rate for adam optimizer
weight_decayFloat5e-5weight decay used in adam optimizer
device_typeStringcudatype of device you want to use for training - cuda or cpu
node_features_propertyStringfeaturesname of features property on nodes from which we read features
edge_features_propertyStringfeaturesname of features property on edges from which we read features
node_label_propertyStringlabelname of label property on nodes from which we read features

Usage:​

 CALL tgn.set_params({learning_type:'self_supervised', batch_size:200, num_of_layers:2,
layer_type:'graph_attn',memory_dimension:20, time_dimension:50,
num_edge_features:20, num_node_features:20, message_dimension:100,
num_neighbors:15, edge_message_function_type:'identity',
message_aggregator_type:'last', memory_updater_type:'gru', num_attention_heads:1});

update(edges)​

This function scrapes data from edges, including edge_features and node_features if they exist, and fills up the batch. If the batch is ready the TGN will process it and be ready to accept new incoming edges.

Input:​

  • edges: mgp.List[mgp.Edges] ➑ List of edges to preprocess (that arrive in a stream to Memgraph). If a batch is full, train or eval starts, depending on the mode.

Usage:​

There are a few options here:

The most convenient one is to create a trigger so that every time an edge is added to the graph, the trigger calls the procedure and makes an update.

CREATE TRIGGER create_embeddings ON --> CREATE BEFORE COMMIT
EXECUTE CALL tgn.update(createdEdges) RETURN 1;

The second option is to add all the edges and then call the algorithm with them:

MATCH (n)-[e]->(m)
WITH COLLECT(e) as edges
CALL tgn.update(edges) RETURN 1;

get()​

Get calculated embeddings for each vertex.

Output:​

  • node: mgp.Vertex ➑ Vertex (node) in Memgraph.
  • embedding: mgp.List[float] ➑ Low-dimensional representation of node in form of graph embedding.

Usage:​

CALL tgn.get() YIELD * RETURN *;

set_eval()​

Change TGN mode to eval.

Usage:​

CALL tgn.set_eval() YIELD *;

get_results()​

This method will return results for every batch you did train or eval on, as well as average_precision, and batch_process_time. Epoch count starts from 1.

Output:​

  • epoch_num:mgp.Number ➑ The number of train or eval epochs.
  • batch_num:mgp.Number ➑ The number of batches per train or eval epoch.
  • batch_process_time:mgp.Number ➑ Time needed to process a batch.
  • average_precision:mgp.Number ➑ Mean average precision on the current batch.
  • batch_type:string ➑ A string indicating whether train or eval is performed on the batch.

Usage:​

CALL tgn.get_results() YIELD * RETURN *;

train_and_eval(num_epochs)​

The purpose of this method is to do additional training rounds on train edges and eval on evaluation edges.

Input:​

  • num_epochs: integer ➑ Perform additional epoch training and evaluation after the stream is done.

Output:​

  • epoch_num: integer ➑ The epoch of the batch for which performance statistics will be returned.
  • batch_num: integer ➑ The number of the batch for which performance statistics will be returned.
  • batch_process_time: float ➑ Processing time in seconds for a batch.
  • average_precision:mgp.Number ➑ Mean average precision on the current batch.
  • batch_type:string ➑ Whether we performed train or eval on the batch.

Usage:​

CALL tgn.train_and_eval(10) YIELD * RETURN *;

The purpose of this method is to get the link prediction score for two vertices in graph if you have been training TGN for the link prediction task.

Input:​

  • src: mgp.Vertex ➑ Source vertex of the link prediction
  • dest: mgp.Vertex ➑ Destination vertex of the link prediction

Output:​

  • prediction: mgp.Number ➑ Float number between 0 and 1, likelihood of link between source vertex and destination vertex.

Usage:​

MATCH (n:User)
WITH n
LIMIT 1
MATCH (m:Item)
OPTIONAL MATCH (n)-[r]->(m)
WHERE r is null
CALL tgn.predict_link_score(n,m) YIELD *
RETURN n,m, prediction;

Example​