Note
Click here to download the full example code
Line Graph Neural Network¶
Author: Qi Huang, Yu Gai, Minjie Wang, Zheng Zhang
In GCN , we demonstrate how to classify nodes on an input graph in a semi-supervised setting, using graph convolutional neural network as embedding mechanism for graph features. In this tutorial, we shift our focus to community detection problem. The task of community detection, i.e. graph clustering, consists of partitioning the vertices in a graph into clusters in which nodes are more “similar” to one another.
To generalize GNN to supervised community detection, Chen et al. introduced a line-graph based variation of graph neural network in Supervised Community Detection with Line Graph Neural Networks. One of the highlight of their model is to augment the vanilla graph neural network(GNN) architecture to operate on the line graph of edge adjacencies, defined with non-backtracking operator.
In addition to its high performance, LGNN offers an opportunity to illustrate how DGL can implement an advanced graph algorithm by flexibly mixing vanilla tensor operations, sparse-matrix multiplication and message- passing APIs.
In the following sections, we will go through community detection, line graph, LGNN, and its implementation.
Supervised Community Detection Task on CORA¶
Community Detection¶
In community detection task, we cluster “similar” nodes instead of “labeling” them. The node similarity is typically described as higher inner density in 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¶
To be consistent with Graph Convolutional Network tutorial, we use CORA to illustrate a simple community detection task. To refresh our memory, CORA is a scientific publication dataset, with 2708 papers belonging to 7 different machine learning sub-fields. Here, we 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.
CORA naturally contains 7 “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:
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.data import citation_graph as citegrh
data = citegrh.load_cora()
G = dgl.DGLGraph(data.graph)
labels = th.tensor(data.labels)
# find all the nodes labeled with class 0
label0_nodes = th.nonzero(labels == 0).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)
print('Intra-class edges percent: %.4f' % (len(intra_src) / len(src_labels)))
Out:
Intra-class edges percent: 0.7680
Binary Community Subgraph from CORA – a Toy Dataset¶
Without loss of generality, in this tutorial we limit the scope of our task to binary community detection.
Note
To create a toy binary-community dataset from CORA, We first extract all two-class pairs from the original CORA 7 classes. For each pair, we treat each class as one community, and find the largest subgraph that at least contain one cross-community edge as the training example. As a result, there are a total of 21 training samples in this mini-dataset.
Here we visualize one of the training samples and its community structure:
import networkx as nx
import matplotlib.pyplot as plt
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)
Interested readers can go to the original paper to see how to generalize to multi communities case.
Community Detection in a Supervised Setting¶
Community Detection problem could be tackled with both supervised and unsupervised approaches. Same as the original paper, we formulate Community Detection in a supervised setting as follows:
- Each training example consists of \((G, L)\), where \(G\) is a directed graph \((V, E)\). For each node \(v\) in \(V\), we assign a ground truth community label \(z_v \in \{0,1\}\).
- The parameterized model \(f(G, \theta)\) predicts a label set \(\tilde{Z} = f(G)\) for nodes \(V\).
- For each example \((G,L)\), the model learns to minimize a specially designed loss function (equivariant loss) \(L_{equivariant} = (\tilde{Z}，Z)\)
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 \(L_{equivariant} = \underset{\pi \in S_c} {min}-\log(\hat{\pi}, \pi)\), where \(S_c\) is the set of all permutations of labels, and \(\hat{\pi}\) is the set of predicted labels, \(- \log(\hat{\pi},\pi)\) denotes negative log likelihood.
For instance, for a toy graph with node \(\{1,2,3,4\}\) and community assignment \(\{A, A, A, B\}\), with each node’s label \(l \in \{0,1\}\),The group of all possible permutations \(S_c = \{\{0,0,0,1\}, \{1,1,1,0\}\}\).
Line Graph Neural network: key ideas¶
An key innovation in this paper is the use of 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’s 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 \(L(G)\) turns an edge of the original graph G into a node. This is illustrated with the graph below (taken from the paper)
Here, \(e_{A}:= （i\rightarrow j）\) and \(e_{B}:= (j\rightarrow k)\) are two edges in the original graph \(G\). In line graph \(G_L\), they correspond to nodes \(v^{l}_{A}, v^{l}_{B}\).
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 \(v^{l}_{A}\), \(v^{l}_{B}\) in lg are connected if the corresponding two edges \(e_{A}, e_{B}\) in g share one and only one node: \(e_{A}\)‘s destination node is \(e_{B}\)‘s source node (\(j\)).
Note
Mathematically, this definition corresponds to a notion called non-backtracking operator: \(B_{(i \rightarrow j), (\hat{i} \rightarrow \hat{j})}\) \(= \begin{cases} 1 \text{ if } j = \hat{i}, \hat{j} \neq i\\ 0 \text{ otherwise} \end{cases}\) where an edge is formed if \(B_{node1, node2} = 1\).
One layer in LGNN – algorithm structure¶
LGNN chains up a series of line-graph neural network layers. The graph representation \(x\) and its line-graph companion \(y\) evolve with the dataflow as follows,
At the \(k\)-th layer, the \(i\)-th neuron of the \(l\)-th channel updates its embedding \(x^{(k+1)}_{i,l}\) with:
Then, the line-graph representation \(y^{(k+1)}_{i,l}\) with,
Where \(\text{skip-connection}\) refers to performing the same operation without the non-linearity \(\rho\), and with linear projection \(\theta_\{\frac{b_{k+1}}{2} + 1, ..., b_{k+1}-1, b_{k+1}\}\) and \(\gamma_\{\frac{b_{k+1}}{2} + 1, ..., b_{k+1}-1, b_{k+1}\}\).
Implement LGNN in DGL¶
General idea¶
The above equations look intimidating. However, we observe the following:
The two equations are symmetric and can be implemented as two instances of the same class with different parameters. Mainly, the first equation operates on graph representation \(x\), whereas the second operates on line-graph representation \(y\). Let us denote this abstraction as \(f\). Then the first is \(f(x,y; \theta_x)\), and the second is \(f(y,x, \theta_y)\). That is, they are parameterized to compute representations of the original graph and its companion line graph, respectively.
Each equation consists of 4 terms (take the first one as an example):
- \(x^{(k)}\theta^{(k)}_{1,l}\), a linear projection of previous layer’s output \(x^{(k)}\), denote as \(\text{prev}(x)\).
- \((Dx^{(k)})\theta^{(k)}_{2,l}\), a linear projection of degree operator on \(x^{(k)}\), denote as \(\text{deg}(x)\).
- \(\sum^{J-1}_{j=0}(A^{2^{j}}x^{(k)})\theta^{(k)}_{3+j,l}\), a summation of \(2^{j}\) adjacency operator on \(x^{(k)}\), denote as \(\text{radius}(x)\)
- \([\{Pm,Pd\}y^{(k)}]\theta^{(k)}_{3+J,l}\), fusing another graph’s embedding information using incidence matrix \(\{Pm, Pd\}\), followed with a linear projection, denote as \(\text{fuse}(y)\).
In addition, each of the terms are performed again with different parameters, and without the nonlinearity after the sum. Therefore, \(f\) could be written as:
\[\begin{split}\begin{split} f(x^{(k)},y^{(k)}) = {}\rho[&\text{prev}(x^{(k-1)}) + \text{deg}(x^{(k-1)}) +\text{radius}(x^{k-1}) +\text{fuse}(y^{(k)})]\\ +&\text{prev}(x^{(k-1)}) + \text{deg}(x^{(k-1)}) +\text{radius}(x^{k-1}) +\text{fuse}(y^{(k)}) \end{split}\end{split}\]Two equations are chained up in the following order :
\[\begin{split}\begin{split} x^{(k+1)} = {}& f(x^{(k)}, y^{(k)})\\ y^{(k+1)} = {}& f(y^{(k)}, x^{(k+1)}) \end{split}\end{split}\]
With these observations, we proceed to implementation. The important point is we are to use different strategies for these terms.
Note
For a detailed explanation of \(\{Pm, Pd\}\), please go to Advanced Topic.
Implementing \(\text{prev}\) and \(\text{deg}\) as tensor operation¶
Since linear projection and degree operation are both simply matrix multiplication, we can write them as PyTorch tensor operation.
In __init__, we define the projection variables:
self.linear_prev = nn.Linear(in_feats, out_feats)
self.linear_deg = nn.Linear(in_feats, out_feats)
In forward(), \(\text{prev}\) and \(\text{deg}\) 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 \(\text{radius}\) as message passing in DGL¶
As discussed in GCN tutorial, we can formulate one adjacency operator as doing one step message passing. As a generalization, \(2^j\) adjacency operations can be formulated as performing \(2^j\) step of message passing. Therefore, the summation is equivalent to summing nodes’ representation of \(2^j, j=0, 1, 2..\) step message passing, i.e. gathering information in \(2^{j}\) neighbourhood of each node.
In __init__, we define the projection variables used in each
\(2^j\) steps of message passing:
self.linear_radius = nn.ModuleList(
[nn.Linear(in_feats, out_feats) for i in range(radius)])
In __forward__, we use following function aggregate_radius() to
gather data from multiple hop. Note that the update_all is called
multiple times.
# 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_src(src='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_src(src='z', out='m'), fn.sum(msg='m', out='z'))
z_list.append(g.ndata['z'])
return z_list
Implementing \(\text{fuse}\) as sparse matrix multiplication¶
\(\{Pm, Pd\}\) is a sparse matrix with only two non-zero entries on each column. Therefore, we construct it as a sparse matrix in the dataset, and implement \(\text{fuse}\) as a sparse matrix multiplication.
in __forward__:
fuse = self.linear_fuse(th.mm(pm_pd, feat_b))
Completing \(f(x, y)\)¶
Finally, we sum up all the terms together, pass it to skip connection and batch-norm.
result = prev_proj + deg_proj + radius_proj + fuse
Then pass result to skip connection:
result = th.cat([result[:, :n], F.relu(result[:, n:])], 1)
Then batch norm
result = self.bn(result) #Batch Normalization.
Below is the complete code for one LGNN layer’s abstraction \(f(x,y)\)
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
Chain up LGNN abstractions as a LGNN layer¶
To implement:
We chain up two LGNNCore instances with different parameter in the forward pass.
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
Chain up LGNN layers¶
We then define an LGNN with three hidden layers.
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)
Training and Inference¶
We first load the data
from torch.utils.data import DataLoader
training_loader = DataLoader(train_set,
batch_size=1,
collate_fn=train_set.collate_fn,
drop_last=True)
We then define the main training loop. Note that each training sample contains
three objects: a DGLGraph, a scipy sparse matrix pmpd and label
array in numpy.ndarray. We first generate the line graph using:
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.
# create the model
model = LGNN(radius=3)
# define the optimizer
optimizer = th.optim.Adam(model.parameters(), lr=1e-2)
# a util 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))
Out:
Epoch 0 | loss 0.5749 | accuracy 0.7292
Epoch 1 | loss 0.5112 | accuracy 0.7639
Epoch 2 | loss 0.4933 | accuracy 0.7726
Epoch 3 | loss 0.4848 | accuracy 0.7702
Epoch 4 | loss 0.4993 | accuracy 0.7784
Epoch 5 | loss 0.4751 | accuracy 0.7875
Epoch 6 | loss 0.4468 | accuracy 0.7939
Epoch 7 | loss 0.4357 | accuracy 0.7990
Epoch 8 | loss 0.4322 | accuracy 0.8059
Epoch 9 | loss 0.4082 | accuracy 0.8233
Epoch 10 | loss 0.4401 | accuracy 0.7986
Epoch 11 | loss 0.4210 | accuracy 0.8049
Epoch 12 | loss 0.3981 | accuracy 0.8310
Epoch 13 | loss 0.4288 | accuracy 0.8037
Epoch 14 | loss 0.4022 | accuracy 0.8290
Epoch 15 | loss 0.4123 | accuracy 0.8210
Epoch 16 | loss 0.4066 | accuracy 0.8182
Epoch 17 | loss 0.4160 | accuracy 0.8174
Epoch 18 | loss 0.3980 | accuracy 0.8222
Epoch 19 | loss 0.3903 | accuracy 0.8339
Visualize training progress¶
We visualize the network’s community prediction on one training example, together with the ground truth.
pmpd1 = sparse2th(pmpd1)
LG1 = G1.line_graph(backtracking=False)
z = model(G1, LG1, pmpd1)
_, pred = th.max(z, 1)
visualize(pred, nx_G1)
Compared with the ground truth. Note that the color might be reversed for the two community as the model is to correctly predict the “partitioning”.
visualize(label1, nx_G1)
Here is an animation to better understand the process. (40 epochs)
Advanced topic¶
Batching¶
LGNN takes a collection of different graphs. Thus, it’s natural we use batching to explore parallelism. Why is it not done?
As it turned out, we moved batching into the dataloader itself.
In the collate_fn for PyTorch Dataloader, we batch graphs using DGL’s
batched_graph API. To refresh our memory, 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. We concatenate
:math`{Pm,Pd}` as block diagonal matrix in correspondence to DGL batched
graph API.
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
You can check out the complete code here.
What’s the business with \(\{Pm, Pd\}\)?¶
Roughly speaking, there is a relationship between how \(g\) and \(lg\) (the line graph) working together with loopy brief propagation. Here, we implement \(\{Pm, Pd\}\) as scipy coo sparse matrix in the dataset, and stack them as tensors when batching. Another batching solution is to treat \(\{Pm, Pd\}\) as the adjacency matrix of a bipartite graph, which maps line graph’s feature to graph’s, and vice versa.
Total running time of the script: ( 0 minutes 46.594 seconds)