Implementation of UniGIN layer from Huang et. al.: UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks.

class topomodelx.nn.hypergraph.unigin_layer.UniGINLayer(in_channels, eps: float = 0.0, train_eps: bool = False, use_norm: bool = False, **kwargs)

Layer of UniGIN.

Implementation of UniGIN layer proposed in [1].

Parameters:

in_channelsint

Dimension of input features.

epsfloat, default=0.0

Constant in GIN Update equation.

train_epsbool, default=False

Whether to make eps a trainable parameter.

use_normbool, default=False

Whether to apply row normalization after the layer.

**kwargsoptional

Additional arguments for the layer modules.

Methods

add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(x_0, incidence_1)	[1]_ initially proposed the forward pass.
get_buffer(target)	Returns the buffer given by target if it exists, otherwise throws an error.
get_extra_state()	Returns any extra state to include in the module's state_dict.
get_parameter(target)	Returns the parameter given by target if it exists, otherwise throws an error.
get_submodule(target)	Returns the submodule given by target if it exists, otherwise throws an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Moves all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor[, persistent])	Adds a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Registers a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Registers a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Registers a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Registers a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Registers a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Adds a parameter to the module.
register_state_dict_pre_hook(hook)	These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	This function is called from load_state_dict() to handle any extra state found within the state_dict.
share_memory()	See torch.Tensor.share_memory_()
state_dict(*args[, destination, prefix, ...])	Returns a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
to_empty(*, device)	Moves the parameters and buffers to the specified device without copying storage.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Moves all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.

__call__

References

[1]

Huang and Yang. UniGNN: a unified framework for graph and hypergraph neural networks. IJCAI 2021. https://arxiv.org/pdf/2105.00956.pdf

[2]

Papillon, Sanborn, Hajij, Miolane. Equations of topological neural networks (2023). awesome-tnns/awesome-tnns

[3]

Papillon, Sanborn, Hajij, Miolane. Architectures of topological deep learning: a survey on topological neural networks (2023). https://arxiv.org/abs/2304.10031

forward(x_0, incidence_1)

[1]_ initially proposed the forward pass.

Its equations are given in [2]_ and graphically illustrated in [3]_.

The forward pass of this layer is composed of three steps.

1. Every hyper-edge sums up the features of its constituent edges:

\[\begin{split}\begin{align*} &🟥 \quad m_{y \rightarrow z}^{(0 \rightarrow 1)} = B_1^T \cdot h_y^{t, (0)}\\ &🟧 \quad m_z^{(0 \rightarrow 1)} = \sum_{y \in \mathcal{B}(z)} m_{y \rightarrow z}^{(0 \rightarrow 1)}\\ \end{align*}\end{split}\]

2. The message to the nodes is the sum of the messages from the incident hyper-edges.

\[\begin{split}\begin{align*} &🟥 \quad m_{z \rightarrow x}^{(1 \rightarrow 0)} = B_1 \cdot m_z^{(0 \rightarrow 1)}\\ &🟧 \quad m_{x}^{(1\rightarrow0)} = \sum_{z \in \mathcal{C}(x)} m_{z \rightarrow x}^{(1\rightarrow0)}\\ \end{align*}\end{split}\]

3. The node features are then updated using the GIN update equation:

\[\begin{split}\begin{align*} &🟩 \quad m_x^{(0)} = m_{x}^{(1\rightarrow0)}\\ &🟦 \quad h_x^{t+1,(0)} = \Theta^t \cdot ((1+\eps)\cdot h_x^{t,(0)}+m_x^{(0)}) \end{align*}\end{split}\]

Parameters:

x_0torch.Tensor, shape = (n_nodes, in_channels)

Input features on the nodes of the hypergraph.

incidence_1torch.sparse, shape = (n_nodes, n_edges)

Incidence matrix mapping edges to nodes (B_1).

Returns:

x_0torch.Tensor

Output node features.

x_1torch.Tensor

Output hyperedge features.