SeqConv

SeqConv is a PyTorch implementation of a graph convolutional operator that uses long short-term memory (LSTM) to update node embeddings. This is useful for graph datasets where each node represents a sequence, such as a time series. Node embeddings are updated like so:

Where φ_r and φ_m are LSTMs (torch.nn.LSTM), and h_Θ is a neural network. The outputs of each LSTM are the last hidden state, h_n, rather than all the output features.

Installation

This module can be installed with pip:

$ pip install seq_conv

Usage

SeqConv is built on PyTorch Geometric and derives from the MessagePassing class. It expects an input graph where each node's "features" is a sequence of vectors. SeqConv, similarly to NNConv, also incorporates any available edge features when collecting messages from a node's neighbors.

Parameters:

• in_channels (int): Number of channels in the input node sequence (e.g. if each node has a sequence of vectors of size n associated with it, then in_channels = n)
• out_channels (int): Number of channels in the output node embedding
• edge_nn (torch.nn.Module): A neural network h_Θ that maps edge features, edge_attr, of shape [-1, num_edge_features] to shape [-1, out_channels]
• aggr (string, optional): The message aggregation scheme to use ("add", "mean", "max")
• root_lstm (bool, optional): If set to False, the layer will not add the LSTM-transformed root node features to the output
• bias (bool, optional): If set to False, the layer will not learn an additive bias
• **kwargs (optional): Additional arguments for torch.nn.LSTM

Example Usage:

import torch
from seq_conv import SeqConv

# Convolutional layer
conv_layer = SeqConv(
    in_channels=1,
    out_channels=5,
    edge_nn=torch.nn.Linear(2, 5)
)

# Your input graph (see: https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html#data-handling-of-graphs)
x = torch.randn((3, 12, 1), dtype=torch.float) # Shape is [num_nodes, seq_len, in_channels]
edge_index = torch.tensor([
    [0, 1, 1, 2],
    [1, 0, 2, 1]
], dtype=torch.long)
edge_attr = torch.randn((4, 2), dtype=torch.long)

# Your output graph
x = conv_layer(x, edge_index, edge_attr) # Shape is now [3, 5]

To-Do: Allow stacking of SeqConv layers.