.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/models/1_gnn/6_line_graph.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_tutorials_models_1_gnn_6_line_graph.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_models_1_gnn_6_line_graph.py:
.. _model-line-graph:
Line Graph Neural Network
=========================
**Author**: `Qi Huang <https://github.com/HQ01>`_, Yu Gai,
`Minjie Wang <https://jermainewang.github.io/>`_, Zheng Zhang
.. warning::
The tutorial aims at gaining insights into the paper, with code as a mean
of explanation. The implementation thus is NOT optimized for running
efficiency. For recommended implementation, please refer to the `official
examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
.. GENERATED FROM PYTHON SOURCE LINES 20-87
In this tutorial, you learn how to solve community detection tasks by implementing a line
graph neural network (LGNN). Community detection, or graph clustering, consists of partitioning
the vertices in a graph into clusters in which nodes are more similar to
one another.
In the :doc:`Graph convolutinal network tutorial <1_gcn>`, you learned how to classify the nodes of an input
graph in a semi-supervised setting. You used a graph convolutional neural network (GCN)
as an embedding mechanism for graph features.
To generalize a graph neural network (GNN) into supervised community detection, a line-graph based
variation of GNN is introduced in the research paper
`Supervised Community Detection with Line Graph Neural Networks <https://arxiv.org/abs/1705.08415>`__.
One of the highlights of the model is
to augment the straightforward GNN architecture so that it operates on
a line graph of edge adjacencies, defined with a non-backtracking operator.
A line graph neural network (LGNN) shows how DGL can implement an advanced graph algorithm by
mixing basic tensor operations, sparse-matrix multiplication, and message-
passing APIs.
In the following sections, you learn about community detection, line
graphs, LGNN, and its implementation.
Supervised community detection task with the Cora dataset
--------------------------------------------
Community detection
~~~~~~~~~~~~~~~~~~~~
In a community detection task, you cluster similar nodes instead of
labeling them. The node similarity is typically described as having higher inner
density within each cluster.
What's the difference between community detection and node classification？
Comparing to node classification, community detection focuses on retrieving
cluster information in the graph, rather than assigning a specific label to
a node. For example, as long as a node is clustered with its community
members, it doesn't matter whether the node is assigned as "community A",
or "community B", while assigning all "great movies" to label "bad movies"
will be a disaster in a movie network classification task.
What's the difference then, between a community detection algorithm and
other clustering algorithm such as k-means? Community detection algorithm operates on
graph-structured data. Comparing to k-means, community detection leverages
graph structure, instead of simply clustering nodes based on their
features.
Cora dataset
~~~~~
To be consistent with the GCN tutorial,
you use the `Cora dataset <https://linqs.soe.ucsc.edu/data>`__
to illustrate a simple community detection task. Cora is a scientific publication dataset,
with 2708 papers belonging to seven
different machine learning fields. Here, you formulate Cora as a
directed graph, with each node being a paper, and each edge being a
citation link (A->B means A cites B). Here is a visualization of the whole
Cora dataset.
.. figure:: https://i.imgur.com/X404Byc.png
:alt: cora
:height: 400px
:width: 500px
:align: center
Cora naturally contains seven classes, and statistics below show that each
class does satisfy our assumption of community, i.e. nodes of same class
class have higher connection probability among them than with nodes of different class.
The following code snippet verifies that there are more intra-class edges
than inter-class.
.. GENERATED FROM PYTHON SOURCE LINES 88-115
.. code-block:: Python
import os
os.environ["DGLBACKEND"] = "pytorch"
import dgl
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from dgl.data import citation_graph as citegrh
data = citegrh.load_cora()
G = data[0]
labels = th.tensor(G.ndata["label"])
# find all the nodes labeled with class 0
label0_nodes = th.nonzero(labels == 0, as_tuple=False).squeeze()
# find all the edges pointing to class 0 nodes
src, _ = G.in_edges(label0_nodes)
src_labels = labels[src]
# find all the edges whose both endpoints are in class 0
intra_src = th.nonzero(src_labels == 0, as_tuple=False)
print("Intra-class edges percent: %.4f" % (len(intra_src) / len(src_labels)))
import matplotlib.pyplot as plt
.. rst-class:: sphx-glr-script-out
.. code-block:: none
NumNodes: 2708
NumEdges: 10556
NumFeats: 1433
NumClasses: 7
NumTrainingSamples: 140
NumValidationSamples: 500
NumTestSamples: 1000
Done loading data from cached files.
/dgl/tutorials/models/1_gnn/6_line_graph.py:102: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
labels = th.tensor(G.ndata["label"])
Intra-class edges percent: 0.6994
.. GENERATED FROM PYTHON SOURCE LINES 116-130
Binary community subgraph from Cora with a test dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Without loss of generality, in this tutorial you limit the scope of the
task to binary community detection.
.. note::
To create a practice binary-community dataset from Cora, first extract
all two-class pairs from the original Cora seven classes. For each pair, you
treat each class as one community, and find the largest subgraph that
at least contains one cross-community edge as the training example. As
a result, there are a total of 21 training samples in this small dataset.
With the following code, you can visualize one of the training samples and its community structure.
.. GENERATED FROM PYTHON SOURCE LINES 130-158
.. code-block:: Python
import networkx as nx
train_set = dgl.data.CoraBinary()
G1, pmpd1, label1 = train_set[1]
nx_G1 = G1.to_networkx()
def visualize(labels, g):
pos = nx.spring_layout(g, seed=1)
plt.figure(figsize=(8, 8))
plt.axis("off")
nx.draw_networkx(
g,
pos=pos,
node_size=50,
cmap=plt.get_cmap("coolwarm"),
node_color=labels,
edge_color="k",
arrows=False,
width=0.5,
style="dotted",
with_labels=False,
)
visualize(label1, nx_G1)
.. image-sg:: /tutorials/models/1_gnn/images/sphx_glr_6_line_graph_001.png
:alt: 6 line graph
:srcset: /tutorials/models/1_gnn/images/sphx_glr_6_line_graph_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Downloading /root/.dgl/cora_binary.zip from https://data.dgl.ai/dataset/cora_binary.zip...
/root/.dgl/cora_binary.zip: 0%| | 0.00/373k [00:00<?, ?B/s]
/root/.dgl/cora_binary.zip: 100%|██████████| 373k/373k [00:00<00:00, 16.3MB/s]
Extracting file to /root/.dgl/cora_binary_2ffdf50c
Done saving data into cached files.
Done saving data into cached files.
.. GENERATED FROM PYTHON SOURCE LINES 159-382
To learn more, go the original research paper to see how to generalize
to multiple communities case.
Community detection in a supervised setting
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The community detection problem could be tackled with both supervised and
unsupervised approaches. You can formulate
community detection in a supervised setting as follows:
- Each training example consists of :math:`(G, L)`, where :math:`G` is a
directed graph :math:`(V, E)`. For each node :math:`v` in :math:`V`, we
assign a ground truth community label :math:`z_v \in \{0,1\}`.
- The parameterized model :math:`f(G, \theta)` predicts a label set
:math:`\tilde{Z} = f(G)` for nodes :math:`V`.
- For each example :math:`(G,L)`, the model learns to minimize a specially
designed loss function (equivariant loss) :math:`L_{equivariant} =
(\tilde{Z}，Z)`
.. note::
In this supervised setting, the model naturally predicts a label for
each community. However, community assignment should be equivariant to
label permutations. To achieve this, in each forward process, we take
the minimum among losses calculated from all possible permutations of
labels.
Mathematically, this means
:math:`L_{equivariant} = \underset{\pi \in S_c} {min}-\log(\hat{\pi}, \pi)`,
where :math:`S_c` is the set of all permutations of labels, and
:math:`\hat{\pi}` is the set of predicted labels,
:math:`- \log(\hat{\pi},\pi)` denotes negative log likelihood.
For instance, for a sample graph with node :math:`\{1,2,3,4\}` and
community assignment :math:`\{A, A, A, B\}`, with each node's label
:math:`l \in \{0,1\}`,The group of all possible permutations
:math:`S_c = \{\{0,0,0,1\}, \{1,1,1,0\}\}`.
Line graph neural network key ideas
------------------------------------
An key innovation in this topic is the use of a line graph.
Unlike models in previous tutorials, message passing happens not only on the
original graph, e.g. the binary community subgraph from Cora, but also on the
line graph associated with the original graph.
What is a line-graph?
~~~~~~~~~~~~~~~~~~~~~
In graph theory, line graph is a graph representation that encodes the
edge adjacency structure in the original graph.
Specifically, a line-graph :math:`L(G)` turns an edge of the original graph `G`
into a node. This is illustrated with the graph below (taken from the
research paper).
.. figure:: https://i.imgur.com/4WO5jEm.png
:alt: lg
:align: center
Here, :math:`e_{A}:= （i\rightarrow j）` and :math:`e_{B}:= (j\rightarrow k)`
are two edges in the original graph :math:`G`. In line graph :math:`G_L`,
they correspond to nodes :math:`v^{l}_{A}, v^{l}_{B}`.
The next natural question is, how to connect nodes in line-graph？ How to
connect two edges? Here, we use the following connection rule:
Two nodes :math:`v^{l}_{A}`, :math:`v^{l}_{B}` in `lg` are connected if
the corresponding two edges :math:`e_{A}, e_{B}` in `g` share one and only
one node:
:math:`e_{A}`'s destination node is :math:`e_{B}`'s source node
(:math:`j`).
.. note::
Mathematically, this definition corresponds to a notion called non-backtracking
operator:
:math:`B_{(i \rightarrow j), (\hat{i} \rightarrow \hat{j})}`
:math:`= \begin{cases}
1 \text{ if } j = \hat{i}, \hat{j} \neq i\\
0 \text{ otherwise} \end{cases}`
where an edge is formed if :math:`B_{node1, node2} = 1`.
One layer in LGNN, algorithm structure
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
LGNN chains together a series of line graph neural network layers. The graph
representation :math:`x` and its line graph companion :math:`y` evolve with
the dataflow as follows.
.. figure:: https://i.imgur.com/bZGGIGp.png
:alt: alg
:align: center
At the :math:`k`-th layer, the :math:`i`-th neuron of the :math:`l`-th
channel updates its embedding :math:`x^{(k+1)}_{i,l}` with:
.. math::
\begin{split}
x^{(k+1)}_{i,l} ={}&\rho[x^{(k)}_{i}\theta^{(k)}_{1,l}
+(Dx^{(k)})_{i}\theta^{(k)}_{2,l} \\
&+\sum^{J-1}_{j=0}(A^{2^{j}}x^{k})_{i}\theta^{(k)}_{3+j,l}\\
&+[\{\text{Pm},\text{Pd}\}y^{(k)}]_{i}\theta^{(k)}_{3+J,l}] \\
&+\text{skip-connection}
\qquad i \in V, l = 1,2,3, ... b_{k+1}/2
\end{split}
Then, the line-graph representation :math:`y^{(k+1)}_{i,l}` with,
.. math::
\begin{split}
y^{(k+1)}_{i',l^{'}} = {}&\rho[y^{(k)}_{i^{'}}\gamma^{(k)}_{1,l^{'}}+
(D_{L(G)}y^{(k)})_{i^{'}}\gamma^{(k)}_{2,l^{'}}\\
&+\sum^{J-1}_{j=0}(A_{L(G)}^{2^{j}}y^{k})_{i}\gamma^{(k)}_{3+j,l^{'}}\\
&+[\{\text{Pm},\text{Pd}\}^{T}x^{(k+1)}]_{i^{'}}\gamma^{(k)}_{3+J,l^{'}}]\\
&+\text{skip-connection}
\qquad i^{'} \in V_{l}, l^{'} = 1,2,3, ... b^{'}_{k+1}/2
\end{split}
Where :math:`\text{skip-connection}` refers to performing the same operation without the non-linearity
:math:`\rho`, and with linear projection :math:`\theta_\{\frac{b_{k+1}}{2} + 1, ..., b_{k+1}-1, b_{k+1}\}`
and :math:`\gamma_\{\frac{b_{k+1}}{2} + 1, ..., b_{k+1}-1, b_{k+1}\}`.
Implement LGNN in DGL
---------------------
Even though the equations in the previous section might seem intimidating,
it helps to understand the following information before you implement the LGNN.
The two equations are symmetric and can be implemented as two instances
of the same class with different parameters.
The first equation operates on graph representation :math:`x`,
whereas the second operates on line-graph
representation :math:`y`. Let us denote this abstraction as :math:`f`. Then
the first is :math:`f(x,y; \theta_x)`, and the second
is :math:`f(y,x, \theta_y)`. That is, they are parameterized to compute
representations of the original graph and its
companion line graph, respectively.
Each equation consists of four terms. Take the first one as an example, which follows.
- :math:`x^{(k)}\theta^{(k)}_{1,l}`, a linear projection of previous
layer's output :math:`x^{(k)}`, denote as :math:`\text{prev}(x)`.
- :math:`(Dx^{(k)})\theta^{(k)}_{2,l}`, a linear projection of degree
operator on :math:`x^{(k)}`, denote as :math:`\text{deg}(x)`.
- :math:`\sum^{J-1}_{j=0}(A^{2^{j}}x^{(k)})\theta^{(k)}_{3+j,l}`,
a summation of :math:`2^{j}` adjacency operator on :math:`x^{(k)}`,
denote as :math:`\text{radius}(x)`
- :math:`[\{Pm,Pd\}y^{(k)}]\theta^{(k)}_{3+J,l}`, fusing another
graph's embedding information using incidence matrix
:math:`\{Pm, Pd\}`, followed with a linear projection,
denote as :math:`\text{fuse}(y)`.
Each of the terms are performed again with different
parameters, and without the nonlinearity after the sum.
Therefore, :math:`f` could be written as:
.. math::
\begin{split}
f(x^{(k)},y^{(k)}) = {}\rho[&\text{prev}(x^{(k-1)}) + \text{deg}(x^{(k-1)}) +\text{radius}(x^{k-1})
+\text{fuse}(y^{(k)})]\\
+&\text{prev}(x^{(k-1)}) + \text{deg}(x^{(k-1)}) +\text{radius}(x^{k-1}) +\text{fuse}(y^{(k)})
\end{split}
Two equations are chained-up in the following order:
.. math::
\begin{split}
x^{(k+1)} = {}& f(x^{(k)}, y^{(k)})\\
y^{(k+1)} = {}& f(y^{(k)}, x^{(k+1)})
\end{split}
Keep in mind the listed observations in this overview and proceed to implementation.
An important point is that you use different strategies for the noted terms.
.. note::
You can understand :math:`\{Pm, Pd\}` more thoroughly with this explanation.
Roughly speaking, there is a relationship between how :math:`g` and
:math:`lg` (the line graph) work together with loopy brief propagation.
Here, you implement :math:`\{Pm, Pd\}` as a SciPy COO sparse matrix in the dataset,
and stack them as tensors when batching. Another batching solution is to
treat :math:`\{Pm, Pd\}` as the adjacency matrix of a bipartite graph, which maps
line graph's feature to graph's, and vice versa.
Implementing :math:`\text{prev}` and :math:`\text{deg}` as tensor operation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Linear projection and degree operation are both simply matrix
multiplication. Write them as PyTorch tensor operations.
In ``__init__``, you define the projection variables.
::
self.linear_prev = nn.Linear(in_feats, out_feats)
self.linear_deg = nn.Linear(in_feats, out_feats)
In ``forward()``, :math:`\text{prev}` and :math:`\text{deg}` are the same
as any other PyTorch tensor operations.
::
prev_proj = self.linear_prev(feat_a)
deg_proj = self.linear_deg(deg * feat_a)
Implementing :math:`\text{radius}` as message passing in DGL
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
As discussed in GCN tutorial, you can formulate one adjacency operator as
doing one-step message passing. As a generalization, :math:`2^j` adjacency
operations can be formulated as performing :math:`2^j` step of message
passing. Therefore, the summation is equivalent to summing nodes'
representation of :math:`2^j, j=0, 1, 2..` step message passing, i.e.
gathering information in :math:`2^{j}` neighborhood of each node.
In ``__init__``, define the projection variables used in each
:math:`2^j` steps of message passing.
::
self.linear_radius = nn.ModuleList(
[nn.Linear(in_feats, out_feats) for i in range(radius)])
In ``__forward__``, use following function ``aggregate_radius()`` to
gather data from multiple hops. This can be seen in the following code.
Note that the ``update_all`` is called multiple times.
.. GENERATED FROM PYTHON SOURCE LINES 382-402
.. code-block:: Python
# Return a list containing features gathered from multiple radius.
import dgl.function as fn
def aggregate_radius(radius, g, z):
# initializing list to collect message passing result
z_list = []
g.ndata["z"] = z
# pulling message from 1-hop neighbourhood
g.update_all(fn.copy_u(u="z", out="m"), fn.sum(msg="m", out="z"))
z_list.append(g.ndata["z"])
for i in range(radius - 1):
for j in range(2**i):
# pulling message from 2^j neighborhood
g.update_all(fn.copy_u(u="z", out="m"), fn.sum(msg="m", out="z"))
z_list.append(g.ndata["z"])
return z_list
.. GENERATED FROM PYTHON SOURCE LINES 403-438
Implementing :math:`\text{fuse}` as sparse matrix multiplication
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:math:`\{Pm, Pd\}` is a sparse matrix with only two non-zero entries on
each column. Therefore, you construct it as a sparse matrix in the dataset,
and implement :math:`\text{fuse}` as a sparse matrix multiplication.
in ``__forward__``:
::
fuse = self.linear_fuse(th.mm(pm_pd, feat_b))
Completing :math:`f(x, y)`
~~~~~~~~~~~~~~~~~~~~~~~~~~
Finally, the following shows how to sum up all the terms together, pass it to skip connection, and
batch norm.
::
result = prev_proj + deg_proj + radius_proj + fuse
Pass result to skip connection.
::
result = th.cat([result[:, :n], F.relu(result[:, n:])], 1)
Then pass the result to batch norm.
::
result = self.bn(result) #Batch Normalization.
Here is the complete code for one LGNN layer's abstraction :math:`f(x,y)`
.. GENERATED FROM PYTHON SOURCE LINES 438-481
.. code-block:: Python
class LGNNCore(nn.Module):
def __init__(self, in_feats, out_feats, radius):
super(LGNNCore, self).__init__()
self.out_feats = out_feats
self.radius = radius
self.linear_prev = nn.Linear(in_feats, out_feats)
self.linear_deg = nn.Linear(in_feats, out_feats)
self.linear_radius = nn.ModuleList(
[nn.Linear(in_feats, out_feats) for i in range(radius)]
)
self.linear_fuse = nn.Linear(in_feats, out_feats)
self.bn = nn.BatchNorm1d(out_feats)
def forward(self, g, feat_a, feat_b, deg, pm_pd):
# term "prev"
prev_proj = self.linear_prev(feat_a)
# term "deg"
deg_proj = self.linear_deg(deg * feat_a)
# term "radius"
# aggregate 2^j-hop features
hop2j_list = aggregate_radius(self.radius, g, feat_a)
# apply linear transformation
hop2j_list = [
linear(x) for linear, x in zip(self.linear_radius, hop2j_list)
]
radius_proj = sum(hop2j_list)
# term "fuse"
fuse = self.linear_fuse(th.mm(pm_pd, feat_b))
# sum them together
result = prev_proj + deg_proj + radius_proj + fuse
# skip connection and batch norm
n = self.out_feats // 2
result = th.cat([result[:, :n], F.relu(result[:, n:])], 1)
result = self.bn(result)
return result
.. GENERATED FROM PYTHON SOURCE LINES 482-493
Chain-up LGNN abstractions as an LGNN layer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To implement:
.. math::
\begin{split}
x^{(k+1)} = {}& f(x^{(k)}, y^{(k)})\\
y^{(k+1)} = {}& f(y^{(k)}, x^{(k+1)})
\end{split}
Chain-up two ``LGNNCore`` instances, as in the example code, with different parameters in the forward pass.
.. GENERATED FROM PYTHON SOURCE LINES 493-506
.. code-block:: Python
class LGNNLayer(nn.Module):
def __init__(self, in_feats, out_feats, radius):
super(LGNNLayer, self).__init__()
self.g_layer = LGNNCore(in_feats, out_feats, radius)
self.lg_layer = LGNNCore(in_feats, out_feats, radius)
def forward(self, g, lg, x, lg_x, deg_g, deg_lg, pm_pd):
next_x = self.g_layer(g, x, lg_x, deg_g, pm_pd)
pm_pd_y = th.transpose(pm_pd, 0, 1)
next_lg_x = self.lg_layer(lg, lg_x, x, deg_lg, pm_pd_y)
return next_x, next_lg_x
.. GENERATED FROM PYTHON SOURCE LINES 507-510
Chain-up LGNN layers
~~~~~~~~~~~~~~~~~~~~
Define an LGNN with three hidden layers, as in the following example.
.. GENERATED FROM PYTHON SOURCE LINES 510-530
.. code-block:: Python
class LGNN(nn.Module):
def __init__(self, radius):
super(LGNN, self).__init__()
self.layer1 = LGNNLayer(1, 16, radius) # input is scalar feature
self.layer2 = LGNNLayer(16, 16, radius) # hidden size is 16
self.layer3 = LGNNLayer(16, 16, radius)
self.linear = nn.Linear(16, 2) # predice two classes
def forward(self, g, lg, pm_pd):
# compute the degrees
deg_g = g.in_degrees().float().unsqueeze(1)
deg_lg = lg.in_degrees().float().unsqueeze(1)
# use degree as the input feature
x, lg_x = deg_g, deg_lg
x, lg_x = self.layer1(g, lg, x, lg_x, deg_g, deg_lg, pm_pd)
x, lg_x = self.layer2(g, lg, x, lg_x, deg_g, deg_lg, pm_pd)
x, lg_x = self.layer3(g, lg, x, lg_x, deg_g, deg_lg, pm_pd)
return self.linear(x)
.. GENERATED FROM PYTHON SOURCE LINES 531-534
Training and inference
-----------------------
First load the data.
.. GENERATED FROM PYTHON SOURCE LINES 534-540
.. code-block:: Python
from torch.utils.data import DataLoader
training_loader = DataLoader(
train_set, batch_size=1, collate_fn=train_set.collate_fn, drop_last=True
)
.. GENERATED FROM PYTHON SOURCE LINES 541-552
Next, define the main training loop. Note that each training sample contains
three objects: A :class:`~dgl.DGLGraph`, a SciPy sparse matrix ``pmpd``, and a label
array in ``numpy.ndarray``. Generate the line graph by using this command:
::
lg = g.line_graph(backtracking=False)
Note that ``backtracking=False`` is required to correctly simulate non-backtracking
operation. We also define a utility function to convert the SciPy sparse matrix to
torch sparse tensor.
.. GENERATED FROM PYTHON SOURCE LINES 552-605
.. code-block:: Python
# Create the model
model = LGNN(radius=3)
# define the optimizer
optimizer = th.optim.Adam(model.parameters(), lr=1e-2)
# A utility function to convert a scipy.coo_matrix to torch.SparseFloat
def sparse2th(mat):
value = mat.data
indices = th.LongTensor([mat.row, mat.col])
tensor = th.sparse.FloatTensor(
indices, th.from_numpy(value).float(), mat.shape
)
return tensor
# Train for 20 epochs
for i in range(20):
all_loss = []
all_acc = []
for [g, pmpd, label] in training_loader:
# Generate the line graph.
lg = g.line_graph(backtracking=False)
# Create torch tensors
pmpd = sparse2th(pmpd)
label = th.from_numpy(label)
# Forward
z = model(g, lg, pmpd)
# Calculate loss:
# Since there are only two communities, there are only two permutations
# of the community labels.
loss_perm1 = F.cross_entropy(z, label)
loss_perm2 = F.cross_entropy(z, 1 - label)
loss = th.min(loss_perm1, loss_perm2)
# Calculate accuracy:
_, pred = th.max(z, 1)
acc_perm1 = (pred == label).float().mean()
acc_perm2 = (pred == 1 - label).float().mean()
acc = th.max(acc_perm1, acc_perm2)
all_loss.append(loss.item())
all_acc.append(acc.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
niters = len(all_loss)
print(
"Epoch %d | loss %.4f | accuracy %.4f"
% (i, sum(all_loss) / niters, sum(all_acc) / niters)
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
/dgl/tutorials/models/1_gnn/6_line_graph.py:561: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:261.)
indices = th.LongTensor([mat.row, mat.col])
/dgl/tutorials/models/1_gnn/6_line_graph.py:562: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated. Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
tensor = th.sparse.FloatTensor(
Epoch 0 | loss 0.5662 | accuracy 0.7168
Epoch 1 | loss 0.4632 | accuracy 0.7869
Epoch 2 | loss 0.4661 | accuracy 0.7697
Epoch 3 | loss 0.4650 | accuracy 0.7766
Epoch 4 | loss 0.4762 | accuracy 0.7747
Epoch 5 | loss 0.4929 | accuracy 0.7740
Epoch 6 | loss 0.4692 | accuracy 0.7904
Epoch 7 | loss 0.4506 | accuracy 0.7957
Epoch 8 | loss 0.4505 | accuracy 0.8016
Epoch 9 | loss 0.4572 | accuracy 0.7885
Epoch 10 | loss 0.4554 | accuracy 0.7876
Epoch 11 | loss 0.4506 | accuracy 0.8054
Epoch 12 | loss 0.4357 | accuracy 0.8028
Epoch 13 | loss 0.4170 | accuracy 0.8170
Epoch 14 | loss 0.4095 | accuracy 0.8164
Epoch 15 | loss 0.4155 | accuracy 0.8116
Epoch 16 | loss 0.4022 | accuracy 0.8235
Epoch 17 | loss 0.4103 | accuracy 0.8172
Epoch 18 | loss 0.4225 | accuracy 0.7993
Epoch 19 | loss 0.3940 | accuracy 0.8235
.. GENERATED FROM PYTHON SOURCE LINES 606-610
Visualize training progress
-----------------------------
You can visualize the network's community prediction on one training example,
together with the ground truth. Start this with the following code example.
.. GENERATED FROM PYTHON SOURCE LINES 610-617
.. code-block:: Python
pmpd1 = sparse2th(pmpd1)
LG1 = G1.line_graph(backtracking=False)
z = model(G1, LG1, pmpd1)
_, pred = th.max(z, 1)
visualize(pred, nx_G1)
.. image-sg:: /tutorials/models/1_gnn/images/sphx_glr_6_line_graph_002.png
:alt: 6 line graph
:srcset: /tutorials/models/1_gnn/images/sphx_glr_6_line_graph_002.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 618-620
Compared with the ground truth. Note that the color might be reversed for the
two communities because the model is for correctly predicting the partitioning.
.. GENERATED FROM PYTHON SOURCE LINES 620-622
.. code-block:: Python
visualize(label1, nx_G1)
.. image-sg:: /tutorials/models/1_gnn/images/sphx_glr_6_line_graph_003.png
:alt: 6 line graph
:srcset: /tutorials/models/1_gnn/images/sphx_glr_6_line_graph_003.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 623-641
Here is an animation to better understand the process. (40 epochs)
.. figure:: https://i.imgur.com/KDUyE1S.gif
:alt: lgnn-anim
Batching graphs for parallelism
--------------------------------
LGNN takes a collection of different graphs.
You might consider whether batching can be used for parallelism.
Batching has been into the data loader itself.
In the ``collate_fn`` for PyTorch data loader, graphs are batched using DGL's
batched_graph API. DGL batches graphs by merging them
into a large graph, with each smaller graph's adjacency matrix being a block
along the diagonal of the large graph's adjacency matrix. Concatenate
:math`\{Pm,Pd\}` as block diagonal matrix in correspondence to DGL batched
graph API.
.. GENERATED FROM PYTHON SOURCE LINES 641-651
.. code-block:: Python
def collate_fn(batch):
graphs, pmpds, labels = zip(*batch)
batched_graphs = dgl.batch(graphs)
batched_pmpds = sp.block_diag(pmpds)
batched_labels = np.concatenate(labels, axis=0)
return batched_graphs, batched_pmpds, batched_labels
.. GENERATED FROM PYTHON SOURCE LINES 652-654
You can find the complete code on Github at
`Community Detection with Graph Neural Networks (CDGNN) <https://github.com/dmlc/dgl/tree/master/examples/pytorch/line_graph>`_.
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 22.600 seconds)
.. _sphx_glr_download_tutorials_models_1_gnn_6_line_graph.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: 6_line_graph.ipynb <6_line_graph.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: 6_line_graph.py <6_line_graph.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: 6_line_graph.zip <6_line_graph.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_