Temporal Graph-Convolutional Layers

A3TGCN(in => out; [bias, init, init_state, add_self_loops, use_edge_weight])

Attention Temporal Graph Convolutional Network (A3T-GCN) model from the paper A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting.

Performs a TGCN layer, followed by a soft attention layer.

Arguments

• in: Number of input features.
• out: Number of output features.
• bias: Add learnable bias. Default true.
• init: Weights' initializer. Default glorot_uniform.
• init_state: Initial state of the hidden stat of the GRU layer. Default zeros32.
• add_self_loops: Add self loops to the graph before performing the convolution. Default false.
• use_edge_weight: If true, consider the edge weights in the input graph (if available). If add_self_loops=true the new weights will be set to 1. This option is ignored if the edge_weight is explicitly provided in the forward pass. Default false.

Examples

julia> a3tgcn = A3TGCN(2 => 6)
A3TGCN(2 => 6)

julia> g, x = rand_graph(5, 10), rand(Float32, 2, 5);

julia> y = a3tgcn(g,x);

julia> size(y)
(6, 5)

julia> Flux.reset!(a3tgcn);

julia> y = a3tgcn(rand_graph(5, 10), rand(Float32, 2, 5, 20));

julia> size(y)
(6, 5)

EvolveGCNO(ch; bias = true, init = glorot_uniform, init_state = Flux.zeros32)

Evolving Graph Convolutional Network (EvolveGCNO) layer from the paper EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs.

Perfoms a Graph Convolutional layer with parameters derived from a Long Short-Term Memory (LSTM) layer across the snapshots of the temporal graph.

Arguments

• in: Number of input features.
• out: Number of output features.
• bias: Add learnable bias. Default true.
• init: Weights' initializer. Default glorot_uniform.
• init_state: Initial state of the hidden stat of the LSTM layer. Default zeros32.

Examples

julia> tg = TemporalSnapshotsGNNGraph([rand_graph(10,20; ndata = rand(4,10)), rand_graph(10,14; ndata = rand(4,10)), rand_graph(10,22; ndata = rand(4,10))])
TemporalSnapshotsGNNGraph:
  num_nodes: [10, 10, 10]
  num_edges: [20, 14, 22]
  num_snapshots: 3

julia> ev = EvolveGCNO(4 => 5)
EvolveGCNO(4 => 5)

julia> size(ev(tg, tg.ndata.x))
(3,)

julia> size(ev(tg, tg.ndata.x)[1])
(5, 10)

DCGRU(in => out, k, n; [bias, init, init_state])

Diffusion Convolutional Recurrent Neural Network (DCGRU) layer from the paper Diffusion Convolutional Recurrent Neural Network: Data-driven Traffic Forecasting.

Performs a Diffusion Convolutional layer to model spatial dependencies, followed by a Gated Recurrent Unit (GRU) cell to model temporal dependencies.

Arguments

• in: Number of input features.
• out: Number of output features.
• k: Diffusion step.
• n: Number of nodes in the graph.
• bias: Add learnable bias. Default true.
• init: Weights' initializer. Default glorot_uniform.
• init_state: Initial state of the hidden stat of the LSTM layer. Default zeros32.

Examples

julia> g1, x1 = rand_graph(5, 10), rand(Float32, 2, 5);

julia> dcgru = DCGRU(2 => 5, 2, g1.num_nodes);

julia> y = dcgru(g1, x1);

julia> size(y)
(5, 5)

julia> g2, x2 = rand_graph(5, 10), rand(Float32, 2, 5, 30);

julia> z = dcgru(g2, x2);

julia> size(z)
(5, 5, 30)

GConvGRU(in => out, k, n; [bias, init, init_state])

Graph Convolutional Gated Recurrent Unit (GConvGRU) recurrent layer from the paper Structured Sequence Modeling with Graph Convolutional Recurrent Networks.

Performs a layer of ChebConv to model spatial dependencies, followed by a Gated Recurrent Unit (GRU) cell to model temporal dependencies.

Arguments

• in: Number of input features.
• out: Number of output features.
• k: Chebyshev polynomial order.
• n: Number of nodes in the graph.
• bias: Add learnable bias. Default true.
• init: Weights' initializer. Default glorot_uniform.
• init_state: Initial state of the hidden stat of the GRU layer. Default zeros32.

Examples

julia> g1, x1 = rand_graph(5, 10), rand(Float32, 2, 5);

julia> ggru = GConvGRU(2 => 5, 2, g1.num_nodes);

julia> y = ggru(g1, x1);

julia> size(y)
(5, 5)

julia> g2, x2 = rand_graph(5, 10), rand(Float32, 2, 5, 30);

julia> z = ggru(g2, x2);

julia> size(z)
(5, 5, 30)

GConvLSTM(in => out, k, n; [bias, init, init_state])

Graph Convolutional Long Short-Term Memory (GConvLSTM) recurrent layer from the paper Structured Sequence Modeling with Graph Convolutional Recurrent Networks.

Performs a layer of ChebConv to model spatial dependencies, followed by a Long Short-Term Memory (LSTM) cell to model temporal dependencies.

Arguments

• in: Number of input features.
• out: Number of output features.
• k: Chebyshev polynomial order.
• n: Number of nodes in the graph.
• bias: Add learnable bias. Default true.
• init: Weights' initializer. Default glorot_uniform.
• init_state: Initial state of the hidden stat of the LSTM layer. Default zeros32.

Examples

julia> g1, x1 = rand_graph(5, 10), rand(Float32, 2, 5);

julia> gclstm = GConvLSTM(2 => 5, 2, g1.num_nodes);

julia> y = gclstm(g1, x1);

julia> size(y)
(5, 5)

julia> g2, x2 = rand_graph(5, 10), rand(Float32, 2, 5, 30);

julia> z = gclstm(g2, x2);

julia> size(z)
(5, 5, 30)

TGCN(in => out; [bias, init, init_state, add_self_loops, use_edge_weight])

Temporal Graph Convolutional Network (T-GCN) recurrent layer from the paper T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction.

Performs a layer of GCNConv to model spatial dependencies, followed by a Gated Recurrent Unit (GRU) cell to model temporal dependencies.

Arguments

• in: Number of input features.
• out: Number of output features.
• bias: Add learnable bias. Default true.
• init: Weights' initializer. Default glorot_uniform.
• init_state: Initial state of the hidden stat of the GRU layer. Default zeros32.
• add_self_loops: Add self loops to the graph before performing the convolution. Default false.
• use_edge_weight: If true, consider the edge weights in the input graph (if available). If add_self_loops=true the new weights will be set to 1. This option is ignored if the edge_weight is explicitly provided in the forward pass. Default false.

Examples

julia> tgcn = TGCN(2 => 6)
Recur(
  TGCNCell(
    GCNConv(2 => 6, σ),                 # 18 parameters
    GRUv3Cell(6 => 6),                  # 240 parameters
    Float32[0.0; 0.0; … ; 0.0; 0.0;;],  # 6 parameters  (all zero)
    2,
    6,
  ),
)         # Total: 8 trainable arrays, 264 parameters,
          # plus 1 non-trainable, 6 parameters, summarysize 1.492 KiB.

julia> g, x = rand_graph(5, 10), rand(Float32, 2, 5);

julia> y = tgcn(g, x);

julia> size(y)
(6, 5)

julia> Flux.reset!(tgcn);

julia> tgcn(rand_graph(5, 10), rand(Float32, 2, 5, 20)) |> size # batch size of 20
(6, 5, 20)