README.md

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.

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.