GAT: Graph Attention Networks¶
Graph Attention Networks (GAT) is a novel architectures that operate on graph-structured data, which leverages masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. Based on PGL, we reproduce GAT algorithms and reach the same level of indicators as the paper in citation network benchmarks.
Simple example to build single head GAT¶
To build a gat layer, one can use our pre-defined pgl.nn.GATConv
or just write a gat layer with message passing interface.
import paddle.fluid as fluid
class CustomGATConv(nn.Layer):
def __init__(self,
input_size, hidden_size,
):
self.hidden_size = hidden_size
self.num_heads = num_heads
self.linear = nn.Linear(input_size, hidden_size)
self.weight_src = self.create_parameter(shape=[ hidden_size ])
self.weight_dst = self.create_parameter(shape=[ hidden_size ])
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
def send_attention(self, src_feat, dst_feat, edge_feat):
alpha = src_feat["src"] + dst_feat["dst"]
alpha = self.leaky_relu(alpha)
return {"alpha": alpha, "h": src_feat["h"]}
def reduce_attention(self, msg):
alpha = msg.reduce_softmax(msg["alpha"])
feature = msg["h"]
feature = feature * alpha
feature = msg.reduce(feature, pool_type="sum")
return feature
def forward(self, graph, feature):
feature = self.linear(feature)
attn_src = paddle.sum(feature * self.weight_src, axis=-1)
attn_dst = paddle.sum(feature * self.weight_dst, axis=-1)
msg = graph.send(
self.send_attention,
src_feat={"src": attn_src,
"h": feature},
dst_feat={"dst": attn_dst})
output = graph.recv(reduce_func=self.reduce_attention, msg=msg)
return output
Datasets¶
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the paper.
Dependencies¶
paddlepaddle==2.0.0
pgl==2.1
Performance¶
We train our models for 200 epochs and report the accuracy on the test dataset.
Dataset |
Accuracy |
---|---|
Cora |
~83% |
Pubmed |
~78% |
Citeseer |
~70% |
How to run¶
For examples, use gpu to train gat on cora dataset.
python train.py --dataset cora
Hyperparameters¶
dataset: The citation dataset “cora”, “citeseer”, “pubmed”.
use_cuda: Use gpu if assign use_cuda.